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. 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 — Comprehensive reference
• Manus: Context Engineering for AI Agents
• Anthropic: Prompt Engineering Techniques
• OpenAI: GPT Best Practices
• Google: Gemini 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