Key Takeaways
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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)
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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
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Testing is Non-Negotiable
- Create evaluation datasets
- Measure everything
- Iterate systematically
- A/B test in production
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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