Day 3 -- Context & Optimization

You can write a solid prompt. But can you fit it in the window? Can you make it better? Can you prove it's better? Day 3 is about the engineering loop: measure what your prompt does, optimize it systematically, and verify that the new version actually wins. Every technique here is quantifiable -- no gut feelings, no "this feels more specific."

Lessons

1. Context Window Management -- token counting, truncation strategies, and the "lost in the middle" problem
2. Prompt Compression -- LLMLingua, summarization chains, and the cost-quality tradeoff
3. Prompt Optimization -- iterative refinement, DSPy, and knowing when to stop
4. Evaluating Prompt Quality -- success metrics, test sets, and LLM-as-judge
5. A/B Testing Prompts -- controlled experiments, statistical significance, and recording results

Previous: Day 2 -- Reasoning & StructureNext: Day 4 -- Advanced Techniques