prompt-optimizer

name

prompt-optimizer

description

Optimize prompts for Claude's latest models (Claude 5 family — Opus 5, Sonnet 5, Fable 5 — plus the still-active Opus 4.8, Sonnet 4.6, and Haiku 4.5) using Anthropic's official prompt-engineering guidance. Use when users want to improve, refine, debug, or create prompts or system prompts for Claude. Triggers include requests to optimize/fix/refine a prompt, make a prompt more effective, write a system prompt, improve instruction following, control verbosity or output formatting, tune effort or thinking depth, fix tool-use/agentic/subagent behavior, migrate prompts to a newer Claude model, or migrate away from prefilled responses. Also use when users report Claude being too verbose, ignoring or partially applying instructions, over- or under-using tools, overthinking, overengineering, spawning too many subagents, hallucinating about code, or producing generic "AI slop" output.

Prompt Optimizer

Optimize prompts for Claude's latest models — the Claude 5 family (Opus 5, Sonnet 5, Fable 5) plus the still-active Opus 4.8, Sonnet 4.6, and Haiku 4.5 — using Anthropic's official guidance. Diagnose what is wrong, pick the right lever, apply a proven fix, then measure.

Two levers, not one

Many problems people try to fix by editing prompt wording are better fixed with an API parameter. Always weigh both:

1. Prompt — wording, structure, examples, role, explicit scope.
2. Parameters — effort (intelligence vs. speed/cost), thinking (adaptive on/off), max_tokens (room to think and act).

On the latest models, effort is frequently the highest-leverage change. If reasoning is shallow on a hard task, raise effort to high/xhigh instead of prompting around it. If the model overthinks or over-explores, lower it. Detail in references/model-tuning.md.

What changed for the latest models

These shifts change how to optimize. Do not carry over old habits blindly:

• Effort governs depth, tool use, and thoroughness. Start xhigh for coding/agentic work, minimum high for intelligence-sensitive work. Lower effort means more literal, more scoped, less tool use.
• Thinking defaults changed with Claude 5. On the Claude 5 family thinking is on by default (Fable 5 rejects an explicit disabled); on Opus 4.8 it stays off unless you set thinking: {type: "adaptive"}. Opus 4.6 and Sonnet 4.6 use adaptive thinking. budget_tokens is removed on Claude 5 / Opus 4.8 — control depth through effort.
• Literal instruction following. The model does what you said, not what you implied. State scope explicitly ("apply to every section, not just the first one").
• Dial back anti-laziness prompting. Aggressive "CRITICAL / You MUST" language needed for older models now causes overtriggering. Use normal phrasing ("Use this tool when...").
• Prefilled assistant responses are no longer supported on 4.6+ models (the request returns a 400). Migrations in references/formatting.md.
• Verbosity is calibrated to task complexity and prose is more direct. Tune with positive examples of the concision you want, not "don't" rules.
• Subagent appetite differs by model. Opus 4.8 under-delegates; Claude Opus 5 delegates readily. Steer explicitly — and cap fan-out on Opus 5.

Effort levels and thinking defaults shift between releases — verify against the current docs at https://docs.claude.com before relying on the specifics here.

Optimization workflow

1. Diagnose the symptom (use the routing table below).
2. Choose the lever — a parameter (effort / thinking / max_tokens) and/or a prompt change.
3. Apply the fix — pull a snippet from the references, or adjust the parameter.
4. Be explicit and positive — say what TO do, give the reason, state the scope, prefer positive examples over negative rules.
5. Measure — build a 10–20 case eval set and A/B the old vs. new prompt with the bundled harness: python scripts/ab_eval.py cases.jsonl --prompt-a old.txt --prompt-b new.txt (judge-based win rate, position-randomized; guide in references/evals.md). Never assume a prompt change helped without checking.

Symptom → fix routing

Symptom	First lever	Reference
Too verbose / over-explains	Positive concision example; lower effort	model-tuning.md, formatting.md
Shallow reasoning on hard task	Raise effort to high/xhigh	model-tuning.md
Overthinks / over-explores / slow	Lower effort; "commit to an approach" snippet	model-tuning.md, agentic.md
Ignores or partially applies an instruction	Make explicit; state scope; positive framing	core principles below
Too much markdown / bullets	Prose-first snippet; match prompt style	formatting.md
Outputs LaTeX, want plain text	Plain-text math snippet	formatting.md
Will not take action (only suggests)	Explicit imperative; default_to_action	patterns.md
Over- or under-uses tools	Adjust effort; explicit tool guidance; dial back "MUST"	patterns.md, model-tuning.md
Not parallelizing tool calls	use_parallel_tool_calls snippet	agentic.md
Spawns too many / too few subagents	Explicit subagent guidance	agentic.md
Overengineers / extra files & abstractions	Anti-overengineering snippet	patterns.md
Hardcodes to pass tests	General-solution snippet	patterns.md
Hallucinates about code	investigate_before_answering snippet	patterns.md
Risky autonomous actions	Reversibility / confirmation snippet	agentic.md
Loses state over long or multi-window tasks	Context-awareness + state-file patterns	agentic.md
Code-review harness misses bugs	Coverage-not-filter snippet	patterns.md
Generic "AI slop" frontend	Concrete spec or propose-options-first; aesthetics snippet	frontend-design.md
Was relying on prefill	Migrate to structured outputs / instructions	formatting.md
Moving to a newer Claude model	Update thinking config; tune anti-laziness	model-tuning.md

Core principles (apply to every prompt)

1. Be clear and direct. Specify the desired output, format, and constraints. Want "above and beyond"? Ask for it explicitly. Golden rule: if a colleague with no context would be confused by the prompt, so will Claude.
   • Less: Create an analytics dashboard
   • More: Create an analytics dashboard. Include as many relevant features and interactions as possible. Go beyond the basics to create a fully-featured implementation.
2. Give the reason. Motivation lets Claude generalize correctly.
   • Less: NEVER use ellipses
   • More: Your response will be read aloud by a text-to-speech engine, so never use ellipses since it will not know how to pronounce them.
3. Use examples (3–5). Make them relevant, diverse, and wrapped in <example> tags. Examples steer format and tone better than description.
4. Structure with XML tags. Use <instructions>, <context>, <input> — consistent, descriptive names, nested when the content is hierarchical.
5. Give a role in the system prompt — even one sentence shifts tone and focus.
6. State scope explicitly. The latest models are literal: "every section, not just the first."
7. Say what TO do, not what NOT to do. Positive framing and positive examples outperform prohibitions.
8. Long context: put long documents at the top, above the query; wrap each in <document> tags; ask for relevant quotes first.

Reference files

Read the file that matches the optimization need:

• references/model-tuning.md — effort levels, adaptive thinking, verbosity, literal instruction following, tone, model identity, computer use, and model-version migration. Read this for any parameter or model-version question.
• references/patterns.md — copy-paste snippet library: action defaults, tool triggering, hallucination prevention, overengineering, hardcoding, research, code-review harnesses, progress updates.
• references/formatting.md — output format and verbosity control, LaTeX, document creation, and prefill migration.
• references/agentic.md — long-horizon reasoning and state, context awareness, multi-window workflows, autonomy & safety, subagents, parallel tools, prompt chaining.
• references/frontend-design.md — overriding the Opus 4.8 design defaults, frontend-aesthetics snippets, and vision / crop-tool tips.
• references/evals.md — building a 10–20 case eval set (case selection, expected_behavior phrasing, leakage, when a win rate is meaningful) and running scripts/ab_eval.py, the A/B harness with a judge-based win rate.