> ## Documentation Index
> Fetch the complete documentation index at: https://aitutorial.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Recap & Resources

> Recap and resources for the Context Engineering & Prompt Design module

## Key Takeaways

1. **Context Engineering is Production Engineering**
   * Structured prompts often reduce hallucinations (results vary)
   * XML tags create clear boundaries
   * Model selection and caching can materially reduce costs (magnitude depends on usage and pricing)

2. **Advanced Techniques When They Matter**
   * Chain-of-Thought: often improves reasoning; magnitude varies by task/model
   * Self-Consistency: additional improvements reported; magnitude varies by task/model
   * Extended Thinking: Enables debugging and transparency

3. **Testing is Non-Negotiable**
   * Create evaluation datasets
   * Measure everything
   * Iterate systematically
   * A/B test in production

4. **Prompt Security**
   * Prompt injection: use role separation and input sanitization
   * Context stuffing: verify data server-side, use XML tags
   * Ambiguous parsing: specify explicit output formats
   * Indirect injection: isolate external content with untrusted data tags
   * Data exfiltration: domain allowlists and tool-layer validation
   * Jailbreaking: fixed identity prompts and input pattern detection
   * Sensitive data leakage: minimal context exposure and output redaction
   * Over-permissioned tools: least privilege and human-in-the-loop gates

5. **Production is Different from Prototyping**
   * Versioning and rollback
   * Monitoring and alerts
   * Cost optimization
   * Safety and validation

## Common Pitfalls to Avoid

❌ "Let me just try different prompts until something works"\
✅ Create eval dataset first, then iterate systematically

❌ "We'll optimize costs later"\
✅ Design for caching from day one

❌ "The model understands my intent"\
✅ Be explicit. Models complete patterns, they don't read minds.

❌ "This worked in testing, ship it"\
✅ A/B test at 10%, then expand

## Additional Resources

### Essential Reading

* DAIR.AI: [Prompt Engineering Guide](https://www.promptingguide.ai/) — Comprehensive reference
* Manus: [Context Engineering for AI Agents](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus)
* Anthropic: [Prompt Engineering Techniques](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview)
* OpenAI: [GPT Best Practices](https://platform.openai.com/docs/guides/prompt-engineering)
* Google: [Gemini Prompting Strategies](https://ai.google.dev/gemini-api/docs/prompting-strategies)

### Tools to Explore

* **LangSmith:** Prompt testing and evaluation
* **LangFuse, Phoenix, Opik:** Monitoring and observability
* **Weights & Biases:** Experiment tracking
* **Helicone:** Cost monitoring and analytics