prompting-claude-fable-5 changedbuild-with-claude/prompt-engineering/prompting-claude-fable-5
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99Claude Fable 5 takes on problems that were previously too complex, long-running, or ambiguous for prior models, and is particularly effective at end-to-end work that takes a person hours, days, or weeks to complete. The teams seeing the best outcomes apply Claude Fable 5 to their hardest unsolved problems; testing it only on simpler workloads tends to undersell its capability range. It also performs reliably on more straightforward tasks.
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11Claude Fable 5 has several behavioral differences from Claude Opus 4.8 that may require prompt or scaffolding updates. Capability improvements at this level are also a good prompt to re-evaluate which instructions, tools, and guardrails are still needed. The patterns below cover the behaviors that most often require tuning.
11Claude Fable 5 has several behavioral differences from Claude Opus 4.8 that might require prompt or scaffolding updates. Capability improvements at this level are also a good prompt to re-evaluate which instructions, tools, and guardrails are still needed. The following patterns cover the behaviors that most often require tuning.
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1313<Note>
1414 For how thinking works on Claude Fable 5 and Claude Mythos 5 (adaptive thinking only, summarized-only thinking output, no extended thinking budgets), see [Thinking](https://platform.claude.com/docs/en/build-with-claude/thinking#configuring-thinking). For the `refusal` stop reason and fallback handling, see [Refusals and fallback](https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback).
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16 Claude Fable 5 runs safety classifiers that target offensive cybersecurity techniques (such as building exploits, malware, or attack tooling), biology and life sciences content (such as lab methods or molecular mechanisms), and extraction of the model's summarized thinking. Benign cybersecurity work and beneficial life sciences tasks may also trigger these safeguards. To re-route declined requests automatically, configure [server-side or client-side fallback](https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback) to Claude Opus 4.8.
16 Claude Fable 5 runs safety classifiers that target offensive cybersecurity techniques (such as building exploits, malware, or attack tooling), biology and life sciences content (such as lab methods or molecular mechanisms), and extraction of the model's summarized thinking. Benign cybersecurity work and beneficial life sciences tasks might also trigger these safeguards. To re-route declined requests automatically, configure [server-side or client-side fallback](https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback) to Claude Opus 4.8.
1717</Note>
1818
1919## Capability improvements
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2424* **First-shot correctness on complex, well-specified problems.** Early testers reported single-pass implementations of systems that previously took days of iteration.
2525* **Vision.** Claude Fable 5 interprets dense technical images, web applications, and detailed screenshots with substantially higher accuracy, often while using fewer output tokens, and is trained to use bash and crop tools to handle flipped, blurry, or noisy images.
2626* **Enterprise workflows.** Claude Fable 5 follows instructions, stays in scope, and produces professional-grade output on financial analysis, spreadsheets, slides, and documents.
27* **Code review and debugging.** Bug-finding recall (outside the cybersecurity domains the safety classifiers cover) is noticeably higher than Claude Opus 4.8, including search across codebases and repository history.
27* **Code review and debugging.** Bug-finding recall (outside the cybersecurity domains the safety classifiers cover) is noticeably higher than Claude Opus 4.8, including search across code bases and repository history.
2828* **Navigating ambiguity.** Claude Fable 5 performs well when given complex, multithreaded requests and asked to determine next steps.
2929* **Delegation and collaboration.** Claude Fable 5 is significantly more dependable at dispatching and sustaining parallel subagents, and reliably manages ongoing communication with long-running subagents and peer agents.
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5151## Strong instruction following
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53Instruction-following is improved enough that you can steer most behaviors with a brief instruction rather than enumerating each behavior by name. For example, when un-steered, Claude Fable 5 can elaborate beyond what the task needs, especially at higher effort settings: surveying options it won't pursue, explaining root causes at length, producing heavily-structured PR descriptions, or writing comments that narrate what the next line does. A short brevity instruction is as effective as listing each pattern:
53Instruction-following is improved enough that you can steer most behaviors with a brief instruction rather than enumerating each behavior by name. For example, when un-steered, Claude Fable 5 can elaborate beyond what the task needs, especially at higher effort settings: surveying options it won't pursue, explaining root causes at length, producing heavily structured PR descriptions, or writing comments that narrate what the next line does. A short brevity instruction is as effective as listing each pattern:
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5555```text wrap
5656Lead with the outcome. Your first sentence after finishing should answer "what happened" or "what did you find": the thing the user would ask for if they said "just give me the TLDR." Supporting detail and reasoning come after. Being readable and being concise are different things, and readability matters more.
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121121## Give the reason, not only the request
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123Claude Fable 5 tends to perform better when it understands the intent behind a request: context lets it connect the task to relevant information rather than inferring intent on its own. Provide context about why you're asking, especially for long-running agents drawing on multiple workstreams:
123Claude Fable 5 tends to perform better when the prompt states the intent behind a request: context lets it connect the task to relevant information rather than inferring intent on its own. Provide context about why you're asking, especially for long-running agents drawing on multiple workstreams:
124124
125125```text wrap
126126I'm working on [the larger task] for [who it's for]. They need [what the output enables]. With that in mind: [request].
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170170## Recommended scaffolding changes
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172* **Start at the top of your difficulty range.** Pick a task harder than what you'd assign to prior models, and have Claude Fable 5 scope it, ask clarifying questions, and execute.
172* **Start at the top of your difficulty range.** Pick a task harder than what you'd assign to prior models, and have Claude Fable 5 scope it, ask clarifying questions, and carry it out.
173173* **Make self-verification explicit in long-run prompts.** Separate, fresh-context verifier subagents tend to outperform self-critique. For long-running tasks, instruct: `Establish a method for checking your own work at an interval of [X] as you build. Run this every [X interval], verifying your work with subagents against the specification.`
174174* **Refactor existing prompts and skills.** Skills developed for prior models are often too prescriptive for Claude Fable 5 and can degrade output quality. Review and consider removing older instructions if default performance is better. Claude Fable 5 also does a good job of updating skills on the fly based on what it learns from the task at hand.
175* **Don't instruct Claude to reproduce its reasoning in the response.** Prompts, skills, or harness instructions that tell the model to echo, transcribe, or explain its internal reasoning as response text may trigger the [`reasoning_extraction` refusal category](https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback#refusal-response) on Claude Fable 5. Server-side fallback with `fallbacks: "default"` doesn't retry requests declined with `reasoning_extraction`; that refusal is returned to you. Audit existing skills and system prompts for reflection or show-your-thinking instructions when migrating. If your application needs reasoning visibility, read the structured `thinking` blocks from [adaptive thinking](https://platform.claude.com/docs/en/build-with-claude/thinking) instead, and use a [send-to-user tool](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-fable-5#create-a-send-to-user-tool) to surface progress during long runs. You can still ask for a short explanation of the answer or a summary of the actions taken; see [Keep reasoning in thinking blocks](https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback#keep-reasoning-in-thinking-blocks).
175* **Don't instruct Claude to reproduce its reasoning in the response.** Prompts, skills, or harness instructions that tell the model to echo, transcribe, or explain its internal reasoning as response text might trigger the [`reasoning_extraction` refusal category](https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback#refusal-response) on Claude Fable 5. Server-side fallback with `fallbacks: "default"` doesn't retry requests declined with `reasoning_extraction`; that refusal is returned to you. Audit existing skills and system prompts for reflection or show-your-thinking instructions when migrating. If your application needs reasoning visibility, read the structured `thinking` blocks from [adaptive thinking](https://platform.claude.com/docs/en/build-with-claude/thinking) instead, and use a [send-to-user tool](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-fable-5#create-a-send-to-user-tool) to surface progress during long runs. You can still ask for a short explanation of the answer or a summary of the actions taken; see [Keep reasoning in thinking blocks](https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback#keep-reasoning-in-thinking-blocks).
176176* **Create a send-to-user tool.** For long, asynchronous agents, a client-side tool delivers messages to the user verbatim without ending the turn. See [Create a send-to-user tool](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-fable-5#create-a-send-to-user-tool).
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