docs(references): add Anthropic + OpenAI official best practices
- anthropic-prompt-caching.md: KV cache mechanics, TTLs, pricing, auto vs explicit - openai-prompt-caching.md: automatic caching, in-memory vs 24h retention, prompt_cache_key - anthropic-prompting-best-practices.md: clear instructions, XML tags, few-shot, model-specific notes - openai-prompting-best-practices.md: message roles, optimization framework, structured outputs, model selection Key findings: - Anthropic caching: only for Claude models, 5m default TTL, 1h optional, 10% cost for reads - OpenAI caching: automatic/free, 5-10min default, 24h extended for GPT-5+ - GLM/ZAI models: neither caching mechanism applies - Subagent model routing table added to openai-prompting-best-practices.md
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# Anthropic — Prompting Best Practices (Claude-specific)
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**Source**: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/claude-prompting-best-practices
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**Fetched**: 2026-03-05
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**Applies to**: Claude Opus 4.6, Sonnet 4.6, Haiku 4.5
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---
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## General Principles
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### Be Clear and Direct
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- Claude responds well to clear, explicit instructions.
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- Think of Claude as a brilliant but new employee — explain your norms explicitly.
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- **Golden rule**: If a colleague with minimal context would be confused by the prompt, Claude will be too.
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- Be specific about output format and constraints.
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- Use numbered lists or bullets when order/completeness matters.
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### Add Context and Motivation
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- Explaining *why* an instruction exists helps Claude generalize correctly.
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- Bad: `NEVER use ellipses`
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- Better: `Your response will be read aloud by TTS, so never use ellipses since TTS won't know how to pronounce them.`
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### Use Examples (Few-shot / Multishot)
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- Examples are one of the most reliable ways to steer format, tone, and structure.
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- 3-5 examples for best results.
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- Wrap examples in `<example>` / `<examples>` tags.
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- Make examples relevant, diverse (cover edge cases), and clearly structured.
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### Structure Prompts with XML Tags
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- Use XML tags to separate instructions, context, examples, and variable inputs.
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- Reduces misinterpretation in complex prompts.
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- Consistent, descriptive tag names (e.g., `<instructions>`, `<context>`, `<input>`).
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- Nest tags for hierarchy (e.g., `<documents><document index="1">...</document></documents>`).
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### Give Claude a Role
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- Setting a role in the system prompt focuses behavior and tone.
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- Even one sentence: `You are a helpful coding assistant specializing in Python.`
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### Long Context Prompting (20K+ tokens)
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- **Put longform data at the top**: Documents and inputs above queries/instructions. Can improve quality up to 30%.
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- **Use XML for document metadata**: Wrap each doc in `<document>` tags with `<source>` and `<document_content>`.
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- **Ground responses in quotes**: Ask Claude to quote relevant sections before analyzing — cuts through noise.
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---
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## Output and Formatting
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### Communication Style (Latest Models — Opus 4.6, Sonnet 4.6)
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- More direct and concise than older models.
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- May skip detailed summaries after tool calls (jumps directly to next action).
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- If you want visibility: `After completing a task with tool use, provide a quick summary of the work.`
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### Control Response Format
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1. **Tell Claude what to do, not what not to do**
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- Not: `Do not use markdown` → Better: `Your response should be flowing prose paragraphs.`
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2. **Use XML format indicators**
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- `Write the prose sections in <smoothly_flowing_prose_paragraphs> tags.`
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3. **Match prompt style to desired output style**
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- If you want no markdown, use no markdown in your prompt.
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4. **For code generation output**: Ask for specific structure, include "Go beyond the basics to create a fully-featured implementation."
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### Minimize Markdown (when needed)
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Put in system prompt:
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```xml
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<avoid_excessive_markdown>
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Write in clear, flowing prose using complete paragraphs. Reserve markdown for inline code, code blocks, and simple headings (##, ###). Avoid **bold** and *italics*. Do NOT use ordered/unordered lists unless presenting truly discrete items or the user explicitly asks. Incorporate items naturally into sentences instead.
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</avoid_excessive_markdown>
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```
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### LaTeX
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Claude Opus 4.6 defaults to LaTeX for math. To disable:
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```
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Format in plain text only. Do not use LaTeX, MathJax, or any markup. Write math with standard text (/, *, ^).
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```
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---
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## Tool Use and Agentic Systems
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### Tool Definition Best Practices
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- Provide clear, detailed descriptions for each tool and parameter.
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- Specify exactly when the tool should and should not be used.
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- Clarify which parameters are required vs. optional.
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- Give examples of correct tool call patterns.
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### Agentic System Prompts
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- Include explicit instructions for error handling, ambiguity, and when to ask for clarification.
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- Specify how to handle tool call failures.
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- Define scope boundaries — what the agent should and should not attempt.
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---
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## Model-Specific Notes
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### Claude Opus 4.6
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- Best for: Complex multi-step reasoning, nuanced analysis, sophisticated code generation.
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- Defaults to LaTeX for math (disable if needed).
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- Most verbose by default — may need to prompt for conciseness.
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### Claude Sonnet 4.6
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- Best for: General-purpose tasks, coding, analysis with good speed/quality balance.
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- More concise than Opus by default.
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- Strong instruction following.
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### Claude Haiku 4.5
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- Best for: Simple tasks, quick responses, high-volume low-stakes workloads.
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- Fastest and cheapest Claude model.
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- May need tighter prompts for complex formatting.
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- Use `<example>` tags more liberally to guide behavior.
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---
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## Relevance for Our Subagent Prompts
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### For Claude models (Haiku/Sonnet as advisors)
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- Always include a role statement.
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- Use XML tags to separate role, instructions, context, and topic.
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- Use examples wrapped in `<example>` tags for structured output formats.
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- Static instructions → early in prompt. Variable topic → at the end.
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### For cheaper models (GLM-4.7, see separate reference)
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- Need even tighter prompting — be more explicit.
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- Structured output schemas > open-ended generation.
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- Constrained output length.
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