25 ChatGPT-5.5 Prompts for Evaluating Claude Fable 5.1 Before a Production Migration

Use ChatGPT-5.5 to Plan the Evaluation, Not to Certify the Migration

Before moving a production workload from Claude Fable 5 to Claude Fable 5.1, treat ChatGPT-5.5 as an independent evaluation-planning partner: useful for structuring test cases, exposing missing evidence, drafting pass/fail criteria, and challenging assumptions. Do not treat its answers as proof that Claude Fable 5.1 is safer, faster, cheaper, or more capable in your environment. The only meaningful migration decision comes from running your own workload against your own prompts, tools, logs, data-handling constraints, and rollback requirements.

This distinction matters because Anthropic’s official documentation describes real Fable 5.1 changes that can affect production behavior. Fable 5.1 uses the model ID claude-fable-5-1, supports a one-million-token context window, allows up to 128K output tokens, and has always-on adaptive thinking with high as the default API effort. Anthropic also says migration from Fable 5 is mostly compatible, but its migration guide identifies breaking changes around forced tool choice, thinking-block compatibility, and edits before existing thinking blocks. A planning assistant can help you convert those facts into tests; it cannot confirm that your actual agent, tool broker, retrieval layer, or governance process will behave correctly.

The prompts in this series are designed for teams that already have a working Claude deployment and need a structured pre-migration evaluation plan. They are not generic “which model is better?” prompts. Each prompt asks ChatGPT-5.5 to request evidence, state assumptions, propose test data, define pass/fail criteria, and identify where human review is required. The output should become a work order for engineering, security, platform operations, legal, or product reviewers—not a substitute for those reviewers.

Operational rule: if a ChatGPT-5.5 answer recommends migration without asking for current prompts, representative tasks, tool schemas, historical traces, acceptance thresholds, privacy rules, and rollback constraints, treat the answer as incomplete and rerun the prompt with stricter evidence requirements.

The Evidence Packet: What ChatGPT-5.5 Needs Before It Can Help

The evaluation packet is the controlled bundle of facts you provide to ChatGPT-5.5 so it can design a migration evaluation instead of guessing. Build this packet before using the 25 prompts. If the packet contains sensitive data, redact or synthesize it according to your organization’s privacy rules; the purpose is to preserve task structure, failure modes, and decision criteria, not to expose unnecessary personal, regulated, contractual, or proprietary content.

How to Frame ChatGPT-5.5’s Role in the Evaluation Workflow

Use ChatGPT-5.5 in a separate planning lane from the actual Fable 5.1 test harness. In the planning lane, it reviews the evidence packet, asks for missing information, proposes scenario coverage, and drafts evaluation rubrics. In the execution lane, your engineering systems run the same tasks against the current production model and Fable 5.1, capture logs, and compare results against predefined thresholds. In the decision lane, human reviewers resolve ambiguous failures, approve risk acceptance, and determine whether the rollout remains blocked, limited, or approved.

A practical workflow is to have ChatGPT-5.5 produce a test matrix, then have a separate automation harness execute that matrix without changing criteria mid-run. This prevents a common failure mode: letting a persuasive planning answer redefine success after results are known. If Fable 5.1 produces better outputs on difficult reasoning tasks but violates a tool-permission boundary, exceeds a cost threshold, or weakens refusal behavior for a regulated workflow, the migration should remain blocked for that workflow until mitigations are tested.

Teams that already maintain an internal evaluation rubric can map these prompts into their existing process rather than starting from scratch. If you need a broader structure for scenario selection, sampling, adjudication, and regression tracking, use as the companion planning reference. The prompts here focus specifically on Fable 5.1 migration readiness, while the framework should own the organization-wide method for how evidence becomes an approval decision. For deeper context on AI Model Evaluation Framework, The Complete GPT-5.5 and GPT-5.6 Model Selection Guide: Choosing Between Sol, Terra, Luna, and GPT-5.5 for Every Use Case is a practical companion. This model-selection guide compares GPT-5.5 with the GPT-5.6 family across workloads and gives readers a practical framework for routing tasks by capability and use case.

Vendor Claims Are Inputs, Not Acceptance Criteria

Anthropic’s Fable 5.1 documentation and announcement provide important starting facts: the model has a larger context window, high default API effort, lower cache-read pricing than Fable 5, and documented migration differences. Anthropic also publishes benchmark and savings claims for Fable 5.1, including reported results on coding and automation-oriented benchmarks. Those claims should shape which risks you test, but they should not replace your acceptance criteria unless your own workload confirms the same pattern under your prompts, tools, and traffic distribution.

For example, an enterprise coding assistant may care less about a broad benchmark score than about whether Fable 5.1 preserves repository-specific constraints, avoids editing unrelated files, batches independent tool calls safely, emits useful progress updates only where supported, and stays within output budgets at higher effort. A customer-support agent may care more about policy consistency, escalation correctness, retrieval-grounded answers, and privacy-safe logging. A security triage workflow may care most about false positives, refusal boundaries, and human review for high-impact actions.

Use to keep vendor-published benchmark numbers in context when stakeholders ask whether public results justify migration. The migration approval itself should still depend on your evidence packet, your test harness, and your risk thresholds. For deeper context on Claude Fable 5 Benchmark Comparison, Claude Fable 5 vs GPT-5.5: Complete Benchmark Comparison and What It Means for AI Developers is a practical companion. The article provides a complete benchmark comparison of Claude Fable 5 and GPT-5.5 and explains the implications for AI developers.

Evidence Packet Template for the 25 Prompts

The following template is intentionally concise so teams can paste a sanitized version into ChatGPT-5.5 before running any of the numbered prompts. Replace bracketed fields with concrete values, and omit information your privacy policy does not allow you to share.

Evaluation packet for Claude Fable 5.1 migration planning

Current production model:
- Model:
- Integration type:
- Critical workflows:
- Known failure modes:

Prompt inventory:
- System/developer prompts:
- User prompt templates:
- Formatting requirements:
- Safety/refusal rules:

Representative task set:
- Routine tasks:
- Difficult reasoning tasks:
- Long-context tasks:
- Tool-using tasks:
- High-impact tasks requiring human review:

Tooling:
- Tool schemas:
- Permission boundaries:
- Side effects:
- Current tool-choice configuration:
- Validation and audit logging:

Conversation and context handling:
- History format:
- Compaction strategy:
- Retry behavior:
- Rollback model compatibility requirements:

Operational logs:
- Typical input/output tokens:
- Cache usage:
- Latency and timeout profile:
- Cost ceilings:

Acceptance thresholds:
- Quality:
- Safety:
- Tool correctness:
- Latency:
- Cost:
- Human-review requirements:

Privacy and retention:
- Data classes:
- Redaction rules:
- Logging restrictions:
- Retention requirements:
- Review owner:

Rollback:
- Feature flags:
- Fallback model:
- Maximum rollback time:
- State or history compatibility risks:

After the packet is ready, run the prompts as a sequence rather than cherry-picking only favorable areas. A safe migration review should include compatibility, effort-level sweeps, tool-call reliability, long-context behavior, cache economics, code-quality regression, safety refusals, privacy and retention review, fallback design, and staged rollout planning.

Prompts 1–9: Compatibility Triage Before You Send Production Traffic

Use these first nine prompts to make ChatGPT-5.5 act as a migration analyst, not as an oracle. Each prompt asks for evidence, assumptions, failure modes, and a human verification step because Anthropic’s Fable 5.1 migration guidance includes specific compatibility constraints: the model ID changes to claude-fable-5-1, forced tool choice with any or a named tool returns a 400 error, older Claude models cannot read Fable 5.1 thinking blocks, and editing content before an existing thinking block can invalidate that block.

1. Prompt: Build a Migration Inventory from Real Request Evidence

Use case: Run this before changing a single model ID. The goal is to inventory every direct Messages API integration, agent harness, gateway, proxy, evaluation job, and platform-specific adapter that might send requests to Fable 5.1. Treat this as the intake step for because missing one batch job or fallback path can produce a migration defect after the main application appears clean. For deeper context on AI API Compatibility Testing, How to Migrate from GPT-5.2 to GPT-5.5 in Production: Complete API Transition Guide with Prompt Compatibility Testing, Cost Optimization, and Rollback Strategies is a practical companion. The article is a production migration guide from GPT-5.2 to GPT-5.5 covering API transition planning, prompt compatibility testing, cost optimization, and rollback strategies.

Copy-ready prompt:

You are my migration inventory analyst for a Claude Fable 5 to Claude Fable 5.1 evaluation. Use only the evidence I provide; do not assume hidden integrations.

Vendor facts to apply:
- Fable 5.1 model ID is claude-fable-5-1.
- Migration is mostly compatible, but forced tool choice using any or a named tool returns a 400 error.
- Older Claude models cannot read Fable 5.1 thinking blocks.
- Changing content before an existing thinking block can invalidate it.
- Anthropic recommends append-only conversation histories and server-side compaction or context editing.

Evidence:
1. Repositories and services: {{REPOSITORY_LIST}}
2. API gateway routes: {{GATEWAY_ROUTES}}
3. Current model IDs and aliases: {{MODEL_ID_LIST}}
4. Tool-calling payload examples: {{TOOL_PAYLOADS}}
5. Conversation persistence format: {{HISTORY_STORAGE}}
6. Batch jobs, eval jobs, and fallback routes: {{OFFLINE_WORKLOADS}}

Produce a migration inventory table with owner, integration type, current model, request features used, Fable 5.1 risk, required code change, test fixture needed, and human reviewer.

Variables: Replace {{REPOSITORY_LIST}} with repository names and owners, {{GATEWAY_ROUTES}} with routing rules, {{MODEL_ID_LIST}} with aliases and hard-coded IDs, {{TOOL_PAYLOADS}} with representative JSON, {{HISTORY_STORAGE}} with schema examples, and {{OFFLINE_WORKLOADS}} with scheduled or batch systems.

Expected output: A table that separates direct API callers, platform wrappers, eval harnesses, fallback chains, and persisted-history consumers. The best answer should identify unknowns instead of pretending the inventory is complete.

Verification step: Compare the table against production logs, dependency graphs, API gateway access logs, and CI secrets usage. Require each service owner to confirm whether the listed request features are accurate.

2. Prompt: Detect Forced Tool Choice That Will Fail on Fable 5.1

Use case: Use this prompt when your existing integration sets tool_choice to any or to a named tool. Anthropic’s migration guide says those forced-tool-choice patterns return a 400 error on Fable 5.1, so the evaluation must find every occurrence before runtime testing.

Copy-ready prompt:

You are reviewing request payloads for Fable 5.1 migration compatibility. Identify every place where forced tool choice is used.

Apply these rules:
- tool_choice: auto is the safe migration target when tools are available.
- tool_choice values that force any tool or a named tool are incompatible with Fable 5.1 and should be treated as blocking defects.
- Do not remove tool schemas from the proposed fix.
- For each defect, propose a test that proves the request no longer forces a tool.

Payload samples and code references:
{{REQUEST_PAYLOADS_AND_CODE}}

Return:
1. Blocking forced-tool-choice findings.
2. Exact JSON paths or code locations.
3. Suggested replacement payload.
4. Risk if the change is not made.
5. Unit or integration test to add.
6. Questions for the tool owner.

Variables: Provide raw JSON request samples, SDK wrapper code, prompt templates, and generated payload snapshots in {{REQUEST_PAYLOADS_AND_CODE}}.

Expected output: A defect list with JSON paths such as tool_choice.type, code references, and replacement examples using auto. The answer should not recommend retrying forced tool choice because the migration guidance identifies it as incompatible.

Verification step: Add a static scan or contract test that fails when payloads contain forced any or named-tool selection for Fable 5.1 routes. Confirm with a negative fixture that the detector catches an intentionally bad payload.

3. Prompt: Replace Forced Tool Choice with Strict Schemas and Structured Outputs

Use case: After finding forced tool choice, use this prompt to preserve reliability without relying on unsupported forcing. Anthropic recommends tool_choice: auto plus strict schemas or structured outputs as a migration pattern, so this step belongs in any review. For deeper context on Structured Tool Calling, ChatGPT 5.5 Instant’s Multi-Step Tool Calling Bug: What Broke, What It Means for MCP Integrations, and How to Work Around It is a practical companion. The article analyzes ChatGPT 5.5 Instant’s multi-step tool-calling bug, how tool calling works, its impact on MCP integrations, and available workarounds.

Copy-ready prompt:

You are designing a Fable 5.1-compatible replacement for forced tool choice. The current workflow forces a tool because downstream systems require deterministic fields.

Current forced-tool request:
{{CURRENT_REQUEST}}

Tool schema:
{{TOOL_SCHEMA}}

Downstream contract:
{{DOWNSTREAM_CONTRACT}}

Design a replacement using tool_choice: auto plus stricter schema and prompt instructions. Include:
1. Revised tool schema with required fields, enum constraints, and validation notes.
2. Revised developer or system instruction that explains when the tool should be used.
3. A fallback behavior when the model does not call the tool.
4. Test cases for valid tool call, no-tool response, malformed arguments, and ambiguous user intent.
5. Pass/fail criteria that a human reviewer can apply.

Variables: Insert the old request in {{CURRENT_REQUEST}}, the current tool definition in {{TOOL_SCHEMA}}, and downstream field requirements in {{DOWNSTREAM_CONTRACT}}.

Expected output: A replacement design that makes the schema more constraining without claiming the model is forced to call a tool. Good outputs include explicit fallback behavior for no-tool responses because auto allows the model to answer without a tool call.

Verification step: Run eval cases for tool-needed, tool-not-needed, ambiguous, and adversarial inputs. Have the application validate arguments independently before executing any external action.

4. Prompt: Find Unsupported Thinking Configuration in Request Builders

Use case: Fable 5.1 has always-on adaptive thinking and a high default API effort according to Anthropic’s overview. Use this prompt to detect legacy request fields, wrappers, or assumptions that try to disable thinking, hard-code unsupported thinking shapes, or confuse stable features with documented beta effort controls.

Copy-ready prompt:

You are auditing thinking-related configuration before a Fable 5.1 evaluation.

Vendor facts to respect:
- Fable 5.1 has always-on adaptive thinking.
- The default API effort is high.
- Per-message effort is documented as a beta capability requiring the mid-conversation-output-config-2026-07-01 beta header.
- Do not treat beta behavior as generally enabled unless my evidence shows the header is intentionally used.

Review this evidence:
{{REQUEST_BUILDERS}}
{{SDK_CONFIG}}
{{BETA_HEADER_USAGE}}
{{EVAL_ASSUMPTIONS}}

Return:
1. Unsupported or suspicious thinking-related fields.
2. Places where the code assumes thinking can be disabled.
3. Places where beta per-message effort is used or implied.
4. Recommended safe baseline configuration for initial tests.
5. Human questions for the platform owner.

Variables: Provide request-construction code, SDK configuration, beta-header middleware, and eval assumptions in the four placeholders.

Expected output: A risk register distinguishing stable Fable 5.1 behavior from beta per-message effort. The answer should flag ambiguous fields for documentation review rather than inventing supported parameters.

Verification step: Inspect generated HTTP payloads from staging, not just source code. Confirm whether any beta header is present intentionally and whether the test plan includes a non-beta baseline.

5. Prompt: Remove Assistant Prefill Dependencies from the Evaluation Path

Use case: Some legacy prompt harnesses rely on assistant prefill or partial assistant text to force response shape. Use this prompt to identify those dependencies and replace them with explicit instructions, schemas, validators, or post-processing before evaluating Fable 5.1. This is especially important when the same harness also stores prior thinking blocks or tool traces.

Copy-ready prompt:

You are auditing a Claude migration for assistant-prefill dependencies. Do not assume prefill is safe for the Fable 5.1 evaluation path. Treat each prefill use as a compatibility risk until verified against the official migration documentation and our generated payloads.

Evidence:
{{PROMPT_TEMPLATES}}
{{MESSAGE_ARRAY_EXAMPLES}}
{{RESPONSE_FORMAT_REQUIREMENTS}}
{{PARSER_CODE}}

For each suspected prefill dependency, provide:
1. Location and example.
2. Why the prefill exists.
3. Whether it controls format, tone, refusal behavior, tool use, or parser stability.
4. Replacement using explicit instructions, stricter schema, structured output, or parser-side validation.
5. Regression test needed before removal.
6. Human approval required.

Variables: Supply prompt templates, message arrays, parser code, and response-format requirements. Include both successful and failed examples if the prefill was introduced to patch a production issue.

Expected output: A categorized list of prefill dependencies and replacements. Strong answers separate cosmetic prefills from parser-critical prefills and identify which removals require downstream contract changes.

Verification step: Generate payload snapshots before and after removal. Confirm that the application can parse responses without relying on hidden assistant text, and add regression tests for malformed or incomplete outputs.

6. Prompt: Map Model IDs Across Direct API, Gateways, and Hosted Platforms

Use case: The official Fable 5.1 model ID is claude-fable-5-1, but production systems often hide model names behind aliases, environment variables, admin consoles, regional routes, or third-party platform catalogs. Use this prompt to prevent a partial migration where one path upgrades and another still sends traffic to an older model.

Copy-ready prompt:

You are creating a model-ID mapping plan for a Fable 5.1 migration.

Known target:
- Direct Anthropic model ID: claude-fable-5-1.

Inventory evidence:
{{ENVIRONMENT_VARIABLES}}
{{MODEL_ROUTING_CONFIGS}}
{{THIRD_PARTY_PLATFORM_CONFIGS}}
{{FALLBACK_RULES}}
{{OBSERVABILITY_LABELS}}

Return a mapping table with:
1. Runtime environment.
2. Caller or platform.
3. Current configured model or alias.
4. Target model or required admin action.
5. Fallback model.
6. Whether Fable 5.1 thinking blocks could later be routed to an older model.
7. Observability label to confirm traffic.
8. Rollback instruction.

Variables: Include environment variables, routing configuration, third-party platform settings, fallback rules, and log labels. Do not omit non-production environments because eval traffic often runs through separate routes.

Expected output: A model-routing matrix that exposes alias drift and fallback hazards. The answer should explicitly flag any path where Fable 5.1-generated conversation history could be consumed by an older Claude model.

Verification step: Send labeled canary requests through every route and compare logs against the expected model mapping. Require a rollback plan that does not route persisted Fable 5.1 thinking blocks to older models.

7. Prompt: Check Thinking-Block Directionality in Fallback and Replay Flows

Use case: Anthropic’s migration guidance says older Claude models cannot read Fable 5.1 thinking blocks. Use this prompt to review fallback, replay, debugging, and offline evaluation flows where a conversation generated by Fable 5.1 might be sent to an older model.

Copy-ready prompt:

You are auditing conversation-history directionality for a Fable 5.1 migration.

Vendor fact:
- Older Claude models cannot read Fable 5.1 thinking blocks.

Review these flows:
{{FALLBACK_FLOW}}
{{REPLAY_FLOW}}
{{EVAL_REUSE_FLOW}}
{{SUPPORT_DEBUG_FLOW}}
{{CONVERSATION_SCHEMA}}

Identify:
1. Any route that sends Fable 5.1 history to an older Claude model.
2. Any replay job that mixes model generations in one conversation.
3. Any support or debugging tool that re-submits stored messages.
4. Safe handling options, such as isolating histories, summarizing without invalid content, or preventing downgrade.
5. Test cases proving downgrade protection works.

Variables: Provide fallback diagrams, replay job descriptions, eval reuse logic, support-tool behavior, and stored conversation schema.

Expected output: A list of one-way compatibility risks and concrete safeguards. Strong answers recommend model-family isolation or safe summarization rather than blindly replaying raw Fable 5.1 histories.

Verification step: Create a replay fixture containing a Fable 5.1 thinking block and confirm the router blocks older-model submission or transforms the history through an approved safe path.

8. Prompt: Diagnose Prefix Mismatch Before Blaming Model Quality

Use case: If a migration test suddenly fails after context editing, do not assume Fable 5.1 reasoning quality regressed. Anthropic warns that changing content before an existing thinking block can invalidate it, and the migration guide identifies a diagnostic beta header, thinking-binding-controls-2026-08-01, for this class of investigation.

Copy-ready prompt:

You are diagnosing a Fable 5.1 migration failure that may be caused by prefix mismatch or invalidated thinking blocks.

Facts to apply:
- Changing content before an existing thinking block can invalidate it.
- Anthropic documents the thinking-binding-controls-2026-08-01 beta header for diagnostics.
- Do not recommend beta diagnostics for production unless explicitly approved.

Evidence:
{{PASSING_TRANSCRIPT}}
{{FAILING_TRANSCRIPT}}
{{HISTORY_MUTATION_CODE}}
{{COMPACTION_SUMMARY}}
{{REQUEST_METADATA}}

Produce:
1. Timeline of what changed before the thinking block.
2. Whether the failure is more consistent with prefix mutation, compaction loss, routing error, or model behavior.
3. Minimal reproduction test.
4. Diagnostic-only use of the beta header, if justified.
5. Production-safe fix.

Variables: Supply a passing transcript, failing transcript, mutation or compaction code, summaries, and request metadata such as model ID and headers.

Expected output: A diagnostic plan that separates history-integrity defects from model-output defects. The answer should treat the beta header as a controlled diagnostic instrument, not as a blanket production dependency.

Verification step: Re-run the failing case with an unmodified append-only history. If it passes, prioritize fixing history mutation before tuning prompts or effort settings.

9. Prompt: Design Append-Only History Tests for Long-Running Agents

Use case: Anthropic recommends append-only conversation histories for Fable 5.1, with server-side compaction or context editing where needed. Use this prompt to turn that recommendation into automated tests for agents that summarize, trim, retry, branch, or resume long-running tasks.

Copy-ready prompt:

You are my test architect for append-only Fable 5.1 conversation history.

Known migration risks:
- Editing content before an existing thinking block can invalidate it.
- Older Claude models cannot read Fable 5.1 thinking blocks.
- Long-running agents may compact, trim, retry, branch, or resume histories.

System evidence:
{{AGENT_HISTORY_SCHEMA}}
{{COMPACTION_LOGIC}}
{{RETRY_LOGIC}}
{{BRANCHING_LOGIC}}
{{PERSISTENCE_LAYER}}

Design an automated test suite with:
1. Unit tests for append-only writes.
2. Integration tests for compaction summaries preserving critical constraints.
3. Retry tests that do not mutate prior messages.
4. Branching tests that isolate histories.
5. Fallback tests preventing older-model reads of Fable 5.1 thinking blocks.
6. Pass/fail criteria and required human review.

Variables: Provide schema definitions, compaction code, retry logic, branching rules, and persistence details. Include any cleanup job that rewrites stored conversations.

Expected output: A test matrix with fixtures, invariants, and failure assertions. The strongest outputs define immutable-history checks and require summaries to preserve critical constraints, tool results, user approvals, and safety boundaries.

Verification step: Run the suite against a seeded conversation containing tool calls, compaction, retries, and a Fable 5.1 thinking block. Confirm that any mutation before the thinking block fails the test before the request reaches the model.

Prompts 10–18: Cost, Context, Tooling, and Workload-Quality Trials

The next nine prompts move the evaluation from compatibility triage into measurable production behavior. Use them after your team has gathered representative transcripts, tool schemas, retrieval logs, code-review examples, long documents, spreadsheets, and current baseline outputs. The goal is not to ask ChatGPT-5.5 whether Claude Fable 5.1 is “better,” but to have it help you design repeatable tests that compare your existing stack against Fable 5.1 under documented constraints, including Anthropic’s one-million-token context window, 128K maximum output, always-on adaptive thinking, high default API effort, beta effort controls, beta progress updates, lower cache-read pricing, and migration rules around append-only histories and tool choice.

10. Prompt: Design an Effort-Level Sweep Before You Lock Production Defaults

Anthropic’s prompting guidance for Fable 5.1 advises teams to benchmark all effort levels rather than assuming the default is optimal. The API default is high effort, while Anthropic says Claude Code defaults to High effort and Claude Cowork and Claude.ai default to Medium effort. This prompt asks ChatGPT-5.5 to turn that guidance into a workload-specific experiment instead of a subjective model bake-off.

Act as an AI-platform evaluation lead. I am testing Claude Fable 5.1 before a production migration.

Build an effort-level sweep plan for these workloads:
[PASTE WORKLOAD LIST: coding agent, retrieval QA, compliance summarization, spreadsheet transformation, customer-support drafting, etc.]

Use the following facts as constraints:
- Fable 5.1 has always-on adaptive thinking.
- The API default effort is high.
- Anthropic recommends benchmarking all effort levels.
- Beta per-message effort uses the mid-conversation-output-config-2026-07-01 beta header.
- Anthropic advises leaving additional output room at xhigh and max effort.

For each workload, produce:
1. Candidate effort levels to test.
2. Minimum representative test cases.
3. Expected failure modes at too-low effort.
4. Expected waste signals at too-high effort.
5. Metrics to collect: correctness, latency observed by user, output tokens, tool calls, retries, human edits, and cost.
6. Pass/fail criteria that do not rely on vendor benchmark claims.
7. A recommendation for when to keep high effort, when to lower effort, and when to escalate effort only for specific turns.

Ask me for any missing production traces before finalizing the plan.

Recommended use: Run this prompt with real task categories and baseline quality data. A useful answer should separate “always high” workloads, such as difficult codebase reasoning, from “low unless uncertainty is detected” workloads, such as simple retrieval or status lookup. Reject any plan that treats Anthropic’s default high effort as proof that high effort is always cost-effective.

11. Prompt: Model Cache-Read Economics with Your Actual Agent Loops

Fable 5.1’s most concrete cost change is cache-read pricing. Anthropic lists base pricing at $10 per million input tokens, $50 per million output tokens, five-minute cache writes at $12.50 per million tokens, one-hour cache writes at $20 per million tokens, and cache reads at $0.25 per million tokens. Anthropic also estimates Fable 5.1 costs 25% less than Fable 5 for typical token-billed workloads because of lower cache-read pricing, with savings up to approximately 45% for highly agentic work. Treat those as vendor estimates until your own logs confirm them. For deeper context on Prompt Caching Cost Guide, Prompt Caching Strategies: 89% Cost Reduction Playbook is a practical companion. The article presents prompt caching strategies for reducing LLM API costs by up to 89% across models including GPT-5.1 and Claude Opus.

Act as a cost-modeling reviewer. I want to estimate whether Claude Fable 5.1 cache-read pricing improves the economics of our agent workflows.

Use these official Fable 5.1 price inputs:
- Input: $10 per million tokens
- Output: $50 per million tokens
- Five-minute cache write: $12.50 per million tokens
- One-hour cache write: $20 per million tokens
- Cache read: $0.25 per million tokens
- Batch API requests receive a 50% input-and-output discount

Analyze the following workflow logs:
[PASTE OR SUMMARIZE: static system prompt tokens, repository/context tokens, repeated policy tokens, average user-turn tokens, output tokens, number of turns, cache hit rate, cache write frequency, retry rate, batch eligibility]

Produce:
1. A simple cost formula for our current baseline and for Fable 5.1.
2. A sensitivity table for cache hit rates of 0%, 25%, 50%, 75%, and 90%.
3. The break-even point where cache writes become worth it.
4. The workflows most likely to benefit from cache reads.
5. The workflows where output tokens dominate and cache savings may not matter.
6. Operational risks: prefix mismatch, history editing, compaction errors, and retry amplification.
7. Evidence we must collect before presenting savings to finance.

Do not present Anthropic's estimated 25% or approximately 45% savings as our measured result unless the supplied logs support it.

Decision rule: Cache economics improve when large stable prefixes are reused many times without invalidating the prefix. If your agent edits prior history, rewrites system instructions, or changes content before existing thinking blocks, the theoretical cache-read advantage may disappear in production traces.

12. Prompt: Stress-Test Long-Context Quality Without Confusing Capacity with Accuracy

Fable 5.1 supports a one-million-token context window and 128K maximum output, but a large context window does not automatically prove that the model will retrieve the right fact, respect late instructions, or preserve all constraints across long documents. This prompt asks ChatGPT-5.5 to design long-context quality tests that measure recall, synthesis, contradiction handling, and output completeness.

Act as a long-context evaluation architect. We are testing Claude Fable 5.1, which has a one-million-token context window and 128K maximum output.

Design a long-context benchmark using our materials:
[PASTE DOCUMENT TYPES: contracts, tickets, repository files, design docs, incident reports, research PDFs, financial workbooks]

Create test cases for:
1. Needle-in-context retrieval across early, middle, and late positions.
2. Cross-document synthesis where the answer requires at least three sources.
3. Contradiction detection between old and new documents.
4. Instruction hierarchy when user instructions conflict with policy or source evidence.
5. Citation or provenance checks if our application requires source grounding.
6. Output truncation risk for large summaries, especially at high, xhigh, or max effort.
7. Compaction-summary fidelity for multi-turn workflows.

For each test, define the input construction method, gold answer, acceptable variance, failure labels, and human-review procedure. Include a warning if a test only proves capacity and not reliability.

Recommended use: Seed the test corpus with facts that are easy for humans to verify but hard for models to locate by pattern alone. Include decoy clauses, superseded documents, repeated names, and similarly formatted tables. Long-context acceptance should require evidence that the model used the correct source segment, not merely that it produced a plausible answer.

13. Prompt: Evaluate User-Visible Progress Updates for Long-Running Workflows

Fable 5.1 adds beta user-visible progress updates between tool calls when applications use thinking.display: updates with the thinking-display-updates-2026-08-18 beta header. The feature can improve user experience during long-running workflows, but the application still owns persistence, timeouts, retries, permissions, and audit logs. This prompt converts progress updates into a UX evaluation instead of treating them as decoration.

Act as a product and platform reviewer. We are evaluating Claude Fable 5.1 beta progress updates for long-running agent tasks.

Known implementation detail:
- Progress updates use thinking.display: updates with the thinking-display-updates-2026-08-18 beta header.

Design a user-visible progress-update test plan for these workflows:
[PASTE: code migration, security triage, document review, multi-source research, spreadsheet reconciliation]

For each workflow, specify:
1. What progress updates should communicate to the user.
2. What progress updates must not reveal, including sensitive tool output or hidden reasoning.
3. When the UI should show a checkpoint, cancellation option, or escalation to a human.
4. How to test whether updates are timely, truthful, and not overconfident.
5. How to handle tool failure, retry, rate limit, or partial completion.
6. Acceptance criteria for user trust, auditability, and operational safety.
7. Copy examples for good, vague, misleading, and unsafe progress messages.

Do not assume the model manages background execution. Separate model output behavior from application orchestration responsibilities.

Operational warning: Progress text can create false confidence if it says “almost done” before critical validation has occurred. Require your harness to distinguish exploratory work, tool execution, verification, and final response generation.

14. Prompt: Test Turn-Scoped Instructions Without Polluting Future Turns

Anthropic documents beta turn-scoped system messages for Fable 5.1 using clear_at: next_user_message with the mid-conversation-system-clear-at-2026-08-21 beta header. This can help teams apply temporary constraints to a single turn, but it must be tested carefully so temporary instructions do not leak into later turns or override durable policy.

Act as a prompt-systems test engineer. We are evaluating Fable 5.1 beta turn-scoped system messages.

Known implementation detail:
- Turn-scoped system messages use clear_at: next_user_message with the mid-conversation-system-clear-at-2026-08-21 beta header.

Create a test suite for turn-scoped instructions across these scenarios:
[PASTE SCENARIOS: redact output for one customer, use terse style for one status update, constrain one code edit to one file, require one-turn legal disclaimer, temporarily disable a tool]

For each scenario, provide:
1. The durable system instruction.
2. The turn-scoped system instruction.
3. The user message that should be affected.
4. The next user message that should not be affected.
5. Expected output for both turns.
6. Failure modes: instruction persistence, instruction loss, conflict with durable policy, or hidden state dependence.
7. Human-review criteria and log fields to inspect.

Also identify cases where a normal user instruction, application-side parameter, or separate session is safer than a turn-scoped system message.

Decision rule: Use turn-scoped instructions for temporary, auditable constraints that should expire automatically. Do not use them as a substitute for durable safety policy, access control, tenant isolation, or application authorization.

15. Prompt: Measure Parallel Tool-Call Behavior and Schema Reliability

Fable 5.1 migration guidance says forced tool choice with any or a named tool returns a 400 error, so production evaluations should focus on tool_choice: auto, strict schemas, structured outputs, and clear tool descriptions. Anthropic’s prompting guide also advises batching independent tool calls. This prompt asks ChatGPT-5.5 to design tests for parallelizable work without assuming that every task should be parallelized.

Act as a tool-calling reliability evaluator. We are migrating a tool-using agent to Claude Fable 5.1.

Use these constraints:
- Forced tool choice with any or a named tool is not supported and returns a 400 error.
- Recommended pattern: tool_choice: auto plus strict schemas or structured outputs.
- Anthropic advises batching independent tool calls where appropriate.

Given our tools:
[PASTE TOOL NAMES, DESCRIPTIONS, JSON SCHEMAS, SIDE EFFECTS, AUTHORIZATION RULES, RATE LIMITS]

Design test cases for:
1. Independent tool calls that can be safely batched.
2. Dependent tool calls that must remain sequential.
3. Ambiguous user requests where the model should ask a clarifying question.
4. Schema-edge cases: missing fields, enum errors, malformed dates, oversized arguments.
5. Side-effecting tools requiring human confirmation.
6. Tool failure, retry, and fallback behavior.
7. Audit evidence proving the agent chose the right tool sequence.

Return a table with test input, expected tool behavior, expected final answer, pass/fail criteria, and required human review.

Recommended use: Include tools that look similar but have different permissions, such as “search tickets” versus “update ticket,” or “quote price” versus “commit order.” Parallel tool-call quality should be judged by correctness, safety, and recoverability, not by the raw number of calls issued in a single turn.

16. Prompt: Compare Code-Review Quality on Real Pull Requests

Anthropic reports vendor-published benchmark results for Fable 5.1, including coding and terminal-task scores, but production code-review quality depends on your repository conventions, test coverage, security requirements, and reviewer expectations. This prompt asks ChatGPT-5.5 to create a code-review evaluation that uses real pull requests and human adjudication rather than relying on benchmark numbers.

Act as a senior engineering manager designing a model-assisted code-review trial for Claude Fable 5.1.

We will test on these repositories and pull requests:
[PASTE REPOSITORY TYPES, LANGUAGES, FRAMEWORKS, REPRESENTATIVE PRS, SECURITY-SENSITIVE AREAS, STYLE GUIDES]

Create an evaluation plan that measures:
1. Bug detection, including concurrency, data validation, authz/authn, migration, and edge-case issues.
2. Security review quality without asking the model to develop exploits.
3. Usefulness of comments: specific, actionable, non-duplicative, and scoped to changed lines.
4. Respect for project conventions and requested review scope.
5. False positives and reviewer fatigue.
6. Missed high-severity issues found by humans.
7. Suggested tests, targeted file edits, and rollback considerations.

Provide a scoring rubric, sample reviewer instructions, minimum PR sample size, adjudication workflow, and pass/fail criteria. Attribute any vendor benchmark claims separately and do not use them as acceptance criteria.

Operational warning: A model can appear impressive by producing many review comments. Require reviewers to label each comment as correct, useful, already covered, speculative, incorrect, or out of scope. Migration approval should depend on reduced reviewer burden and caught defects, not comment volume.

17. Prompt: Build a Multistep Research Trial with Source Discipline

Anthropic recommends asking for search at low effort when needed and batching independent tool calls where possible. For production research assistants, the evaluation should test planning, source selection, contradiction handling, citation discipline, and escalation when evidence is insufficient. This prompt frames Fable 5.1 as one component in a research system whose tools and policies must be measured.

Act as a research-operations evaluator. We are testing Claude Fable 5.1 for multistep research workflows.

Our research tools and sources are:
[PASTE SEARCH TOOLS, INTERNAL KNOWLEDGE BASES, APPROVED EXTERNAL SOURCES, CITATION RULES, RECENCY REQUIREMENTS]

Design a trial for these research tasks:
[PASTE TASKS: vendor due diligence, incident postmortem research, policy comparison, technical architecture survey, market scan]

For each task, specify:
1. Planning steps the model should perform before searching.
2. Which searches can run independently and which depend on earlier findings.
3. Evidence-quality rules: primary source preference, date checks, source conflicts, and uncertainty labels.
4. Required citations or provenance fields.
5. When low effort is sufficient and when higher effort is justified.
6. Criteria for saying “not enough evidence.”
7. Human-review checkpoints before external publication or executive use.

Return a test matrix with task, tools, expected evidence, failure modes, scoring rubric, and acceptance threshold.

Decision rule: A strong research answer should preserve uncertainty rather than flattening conflicting sources into a confident synthesis. Penalize unsupported claims, missing dates, overbroad generalizations, and failure to distinguish primary documentation from commentary.

18. Prompt: Evaluate Document and Spreadsheet Tasks with Deterministic Checks

Fable 5.1 supports long document creation and large outputs, but document and spreadsheet workflows still need deterministic validation. Models can summarize, transform, and explain; your application or human reviewers should verify formulas, totals, formatting requirements, source mapping, and approval rules. This prompt asks ChatGPT-5.5 to turn office-style tasks into auditable tests.

Act as an enterprise workflow QA lead. We are evaluating Claude Fable 5.1 for document and spreadsheet tasks before migration.

Representative tasks:
[PASTE: contract summary, board memo drafting, policy comparison, CSV cleanup, spreadsheet reconciliation, formula explanation, invoice exception report]

Create an evaluation suite that includes:
1. Input fixtures, including messy tables, merged cells, missing values, duplicate rows, and conflicting document sections.
2. Expected outputs with deterministic checks: row counts, totals, formula references, required headings, citations, and redaction rules.
3. Tests for preserving source meaning while improving formatting.
4. Tests for refusing or escalating when data is insufficient.
5. Maximum-output planning for long memos or large transformed files.
6. Human-review steps for financial, legal, HR, or regulated documents.
7. Acceptance criteria for correctness, traceability, edit distance, and reviewer time saved.

Produce a table with task, fixture, expected result, validation method, severity of failure, and owner for final approval. Do not present this as legal, financial, or compliance advice.

Recommended use: Pair model-generated outputs with deterministic validators wherever possible. For spreadsheets, compare totals, row identities, formula references, and exception counts. For documents, compare required clauses, citations, redactions, and approval language. The model evaluation should prove that Fable 5.1 fits your review workflow, not that it can replace accountable business owners.

Prompts 19–25: Safety, Governance, Rollout, and the Final Migration Decision

Prompts 19–25 move the evaluation from model behavior into production governance. At this stage, ChatGPT-5.5 should not be asked to “approve” Claude Fable 5.1; it should help your team structure evidence, expose missing tests, and force explicit ownership for safeguards, retention, observability, and rollback decisions. Anthropic’s Fable 5.1 documentation describes major migration concerns such as forced-tool-choice errors, append-only history requirements around thinking blocks, always-on adaptive thinking, long-context operation, and beta controls for per-message effort and progress updates. These final prompts convert those facts into operational acceptance gates.

19. Prompt: Measure Safeguard False Positives Against Legitimate Security Work

Anthropic has publicly stated that Fable 5.1 has fewer cybersecurity false positives than its previous safeguards, but that statement is a vendor claim, not your acceptance result. Use this prompt to test whether legitimate internal work—such as vulnerability triage, secure-code review, incident reconstruction, and defensive detection engineering—is incorrectly blocked, diluted, or redirected in your environment.

You are helping our security, AI-platform, and application teams evaluate Claude Fable 5.1 before production migration.

Task: Design a safeguard false-positive test plan for legitimate defensive cybersecurity workflows.

Use the following evidence packet:
- Current model and target model: [insert current model and claude-fable-5-1]
- Allowed security tasks: [secure code review, vulnerability explanation, detection logic, incident response, etc.]
- Disallowed tasks under our policy: [exploit development, credential theft, destructive instructions, etc.]
- 20–50 historical examples of allowed security prompts: [paste or summarize]
- Current-model outputs and human ratings: [paste results]
- Anthropic documentation or claims we are treating as inputs, not proof: [paste citations or notes]

Produce:
1. A test matrix separating allowed defensive work from disallowed offensive requests.
2. A definition of “false positive” for our environment.
3. Pass/fail thresholds for refusal rate, excessive hedging, missing remediation detail, and policy-consistent redirection.
4. A human-review rubric for security engineers.
5. Assumptions and missing evidence that could invalidate the test.
6. A final recommendation on whether Fable 5.1 is acceptable for these workflows, stated as conditional on evidence rather than vendor claims.

Implementation guidance: Include borderline but legitimate prompts, such as “explain why this proof-of-concept is dangerous without improving it” or “write a detection rule for this observed behavior.” Do not grade the model down for refusing to provide exploit-development steps if your policy forbids them. The purpose is to find over-refusal on authorized defensive work, not to weaken safety controls.

20. Prompt: Define Refusal, Redirection, and Fallback Policy Before Launch

A production migration needs a deterministic policy for what happens when Fable 5.1 refuses, partially complies, asks for more context, or produces a safe alternative. This matters because fallback chains can accidentally bypass the very safeguard that stopped the first response. It also matters for thinking-block directionality: older Claude models cannot read Fable 5.1 thinking blocks, so replay and fallback flows need explicit compatibility handling.

You are our AI safety and platform policy reviewer.

Task: Draft a refusal, redirection, and fallback policy for migrating selected workloads to Claude Fable 5.1.

Evidence packet:
- Workload categories: [customer support, coding, security, research, document analysis, etc.]
- Current fallback models or routes: [insert]
- Conversation replay behavior: [append-only, edited history, compacted summaries, etc.]
- Known Fable 5.1 migration constraints: forced tool choice with "any" or named tools returns 400; older models cannot read Fable 5.1 thinking blocks; changing content before an existing thinking block can invalidate it.
- Internal safety policy: [paste]
- Human escalation paths: [paste]

Produce:
1. A taxonomy of refusal, safe completion, partial completion, clarification request, and safe redirection.
2. Rules for when fallback is allowed, blocked, or requires human approval.
3. A compatibility rule for removing or transforming Fable 5.1-specific thinking blocks before using older models.
4. Logging fields needed for audit without storing unnecessary sensitive content.
5. Five test cases where fallback would be unsafe.
6. Pass/fail criteria and owner sign-off requirements.

Implementation guidance: Treat “try another model” as a governed decision, not a hidden retry. If the original model refused because the request crossed policy boundaries, the fallback system should preserve the refusal context and route to human review or a safe explanation, rather than attempting to obtain the prohibited content elsewhere. For deeper context on AI Agent Safety Testing, 30 ChatGPT Prompts for AI Agent Safety Testing: Red-Team Your Autonomous Systems Before They Red-Team You is a practical companion. The article offers 30 ChatGPT prompts for red-teaming autonomous AI agents across containment, privilege escalation, and other safety-testing categories.

21. Prompt: Review Data Retention, Logging, and Privacy Assumptions

Data-retention review is an engineering and governance exercise, not a substitute for legal advice. Anthropic’s public materials state that eligible customers may use Fable 5.1 with zero data retention until Enterprise Frontier Safeguards becomes available, and that Enterprise Frontier Safeguards is phased rather than universally available. Your team should verify its own contract, workspace settings, cloud architecture, logging pipeline, and support workflows before sending regulated or confidential workloads.

You are assisting our privacy, security, legal, and platform teams. Do not provide legal advice; produce an engineering review checklist and evidence plan.

Task: Evaluate data retention, logging, and privacy readiness for a Claude Fable 5.1 production migration.

Evidence packet:
- Anthropic account type and contractual retention terms: [insert verified source]
- Whether zero data retention is enabled or available to us: [insert evidence]
- Whether any phased Enterprise Frontier Safeguards program applies: [insert evidence or "not applicable"]
- Data classes in scope: [PII, PHI, source code, secrets, customer tickets, logs, etc.]
- Application logs and traces currently captured: [insert fields]
- Prompt caching and batch usage plans: [insert]
- Support and incident-access processes: [insert]

Produce:
1. A data-flow diagram in text form from user input to model request, cache, logs, traces, analytics, and human review.
2. A list of sensitive fields that must be redacted, tokenized, minimized, or excluded.
3. Open questions for legal and procurement.
4. Technical controls the platform team can implement immediately.
5. Evidence required before approving regulated or confidential data.
6. A pass/fail recommendation with named owners for unresolved issues.

Implementation guidance: Do not assume “zero retention” from a blog post or model name. Ask for account-level proof, data-processing terms, and the actual request path used by your gateway, observability vendor, and application logs. Retention can also occur outside the model provider if your own traces, prompt stores, error queues, or replay systems capture full payloads.

22. Prompt: Specify Observability for Quality, Cost, Safety, and Migration Errors

Fable 5.1 evaluations should capture more than success rate. The migration guide identifies concrete failure modes, including 400 errors from forced tool choice, invalidated thinking blocks after earlier content changes, and fallback incompatibility with older models. The prompting guide also recommends benchmarking effort levels, batching independent tool calls, preserving append-only conversations, and leaving extra output room at higher effort settings. Observability should make those behaviors measurable.

You are our production observability architect.

Task: Define the telemetry plan for a Claude Fable 5.1 migration.

Evidence packet:
- Workloads and service boundaries: [insert]
- Request builder fields: [model ID, effort, tool_choice, cache settings, max output, beta headers if any]
- Tool-call architecture: [insert]
- Existing metrics and traces: [insert]
- Safety and refusal policy from Prompt 20: [insert]
- Cost model assumptions: [insert official prices and internal estimates]

Produce:
1. Metrics for request success, latency distribution, token usage, cache writes, cache reads, output length, retries, and batch usage.
2. Migration-specific error counters, including forced-tool-choice 400s and thinking-block replay issues.
3. Quality signals that require human labels rather than automated guesses.
4. Safety signals for refusals, redirections, policy escalations, and fallback attempts.
5. Dashboards for engineering, security, finance, and product owners.
6. Alert thresholds for rollback, traffic freeze, or human review.
7. A minimal log schema that avoids unnecessary sensitive content.

Implementation guidance: Separate provider-level behavior from harness-level behavior. Your application owns retries, persistence, tool permissions, audit logging, and rollback. If an agent edits prior conversation history, hides tool errors, or compacts away critical constraints, the observability layer should identify that as an orchestration issue rather than simply labeling the model unreliable.

23. Prompt: Build Adversarial Test Cases for Policy, Tools, and Long Context

Adversarial testing should target the exact places migration can fail: tool schemas, long-context retrieval, policy boundaries, prompt injection, stale compacted summaries, and unsafe fallback. For long-context tests, do not confuse the one-million-token context window with guaranteed attention to every detail. The test should include buried constraints, conflicting instructions, and irrelevant distractors, then grade against deterministic facts.

You are our adversarial evaluation designer.

Task: Create an adversarial test suite for Claude Fable 5.1 migration readiness.

Evidence packet:
- High-risk workflows: [insert]
- Tool schemas and permissions: [insert]
- System prompts and developer instructions: [insert]
- Long-context documents or repositories: [insert]
- Safety policy and refusal rules: [insert]
- Known migration constraints: [insert]

Produce:
1. At least 30 adversarial cases grouped by prompt injection, tool misuse, schema ambiguity, hidden long-context constraints, unsafe fallback, and data-exfiltration attempts.
2. For each case, the expected safe behavior and unacceptable behavior.
3. Required test data and any synthetic data that must be created.
4. Automated checks for deterministic outputs and human-review checks for judgment-heavy outputs.
5. Severity levels and stop-the-rollout conditions.
6. Assumptions, blind spots, and recommended retest cadence.

Implementation guidance: Include cases where the user asks the model to ignore earlier policy, where a retrieved document contains malicious instructions, and where a tool result conflicts with the user’s goal. If your application can perform irreversible actions, adversarial tests should confirm that human checkpoints stop payment, deletion, credential, deployment, or customer-notification actions until an authorized reviewer approves.

24. Prompt: Design a Staged Rollout with Rollback and Human Checkpoints

A staged rollout should isolate risk by workload, tenant, data class, and action authority. Claude Managed Agents may require only a model-name change according to Anthropic’s migration guidance, while direct Messages API integrations can require request-builder changes, append-only history handling, and tool-choice updates. Your rollout should distinguish those paths rather than treating migration as a single switch.

You are our release manager for the Claude Fable 5.1 migration.

Task: Create a staged rollout plan with evidence gates and rollback rules.

Evidence packet:
- Workload inventory and risk tiers: [insert]
- Integration type: [direct API, managed agent, Claude Code, gateway, etc.]
- Current production metrics: [insert]
- Completed results from Prompts 1–23: [insert summaries]
- Human-review capacity: [insert]
- Rollback mechanism and model fallback constraints: [insert]
- Customer or internal-user communication requirements: [insert]

Produce:
1. A rollout sequence from offline eval to shadow mode, limited pilot, percentage ramp, and general production use.
2. Entry and exit criteria for each stage.
3. Metrics that must improve, remain neutral, or stay within tolerance.
4. Rollback triggers for quality, safety, cost, latency, privacy, and tool errors.
5. Human checkpoints for high-impact actions and policy-sensitive outputs.
6. A communications plan for support, security, finance, and product owners.
7. A final list of unresolved risks that must be accepted explicitly or fixed before launch.

Implementation guidance: Use shadow traffic only where your privacy and retention rules allow it, and avoid sending duplicate sensitive data just to collect benchmarks. During early pilots, keep human review close to the workflow, not in a weekly spreadsheet after incidents have already reached users. Rollback should be tested as a procedure before launch, including conversation-state compatibility and handling of Fable 5.1 thinking blocks.

25. Prompt: Produce the Final Go/No-Go Decision Memo

The final decision should not be a vibes-based comparison or a vendor-benchmark recital. Anthropic-published benchmark figures and cost claims can inform hypotheses, but your go/no-go memo should rely on your own workload results, contracts, logs, human ratings, and incident drills. The memo should separate “approved for this workload” from “approved everywhere.”

You are the neutral chair of our Fable 5.1 migration review board.

Task: Draft a final go/no-go decision memo using only the evidence we provide.

Evidence packet:
- Results from compatibility, cost, quality, safety, privacy, observability, adversarial, and rollout tests: [insert]
- Vendor claims we considered but did not treat as independent proof: [insert]
- Open risks and owner responses: [insert]
- Human-review findings: [insert]
- Production-readiness checklist: [insert]
- Proposed scope of launch: [insert workloads, users, regions, and data classes]

Produce:
1. Executive decision: Go, limited go, no-go, or defer.
2. Scope of approval and explicit exclusions.
3. Evidence supporting the decision.
4. Failed tests and mitigation status.
5. Residual risks accepted by named owners.
6. Required monitoring for the first 7, 30, and 90 days.
7. Rollback plan summary.
8. Sign-off table for engineering, security, legal/privacy, finance, product, and executive owner.

Implementation guidance: If the memo cannot name owners, evidence, and rollback triggers, the decision is not ready. A limited go is often the right outcome when Fable 5.1 performs well for long-horizon coding or research tasks but still lacks approval for regulated data, autonomous tool actions, or security-sensitive workflows.

Evaluation Scorecard for Prompts 19–25

Evidence Standards for the Final Review

• Use real workload samples where permitted. Synthetic tests are useful for coverage, but they should not replace historical production examples, real tool schemas, actual documents, and prior incident patterns.
• Keep vendor claims separate from measured results. Anthropic documentation and announcements can define features, constraints, and published claims; your scorecard should mark them as source inputs unless independently measured in your environment.
• Require reproducibility. Store prompt versions, request parameters, model IDs, beta headers when used, tool schemas, output artifacts, evaluator identities, and rating rubrics so a failed or disputed result can be reconstructed.
• Label human judgment explicitly. Code quality, safe redirection, research usefulness, and business correctness often require expert review. Do not hide subjective ratings behind a single automated score.
• Document exclusions. If regulated data, autonomous actions, customer-facing support, or cybersecurity tasks were not tested, the approval should exclude those uses until they pass their own review.

Human-Review Workflow for Migration Approval

1. Platform engineering verifies request construction, model IDs, tool-choice behavior, append-only history, cache behavior, and rollback mechanics.
2. Security reviews safeguard tests, adversarial prompts, tool permissions, fallback behavior, and incident-response observability.
3. Privacy or legal reviewers confirm retention assumptions, data classifications, logging minimization, and contractual evidence without relying on model documentation alone.
4. Domain experts rate task quality using workload-specific rubrics for code, research, documents, customer operations, or internal automation.
5. Finance or FinOps checks token usage, cache-read assumptions, batch eligibility, and expected variance under staged rollout.
6. The accountable product or executive owner accepts residual risk, limits launch scope, and signs the rollback plan before production traffic expands.

Conclusion: Treat Migration as a Controlled System Change

Claude Fable 5.1 can be evaluated as a production candidate only when engineering evidence, safety behavior, privacy posture, and operational readiness are tested together. The final seven prompts are designed to prevent a common failure mode: teams prove that a model can answer impressive tasks, then discover after launch that refusals, logs, fallback, tool permissions, or rollback were never governed. A strong go decision should name the approved workloads, the excluded workloads, the evidence supporting both, and the people responsible for monitoring the first production stages.

Useful Links

• Claude Fable 5.1 overview — Anthropic documentation
• Claude Fable 5.1 migration guide — Anthropic documentation
• Prompting Claude Fable 5.1 — Anthropic documentation