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Context Engineering for AI Systems

7 chapters · 30 lessons

1. 1. Token Budgets and Context Limits

   4 lessons

   Understand how tokenization works, measure actual token consumption, and predict costs before sending requests.

2. 2. Designing Effective Context Selection

   5 lessons

   Learn strategies to choose what information belongs in the prompt and what should be excluded or retrieved separately.

3. 3. Context Compression Techniques

   4 lessons

   Apply summarization, extraction, and chunking methods to fit more information into limited token budgets.

4. 4. Long Context vs Retrieval Strategies

   5 lessons

   Decide when to use large context windows, when to implement retrieval augmented generation, and when to combine both.

5. 5. Protecting Sensitive Data in Context

   4 lessons

   Implement redaction, access controls, and architectural patterns to prevent data leakage through model prompts.

6. 6. Context Management for Agentic Workflows

   4 lessons

   Design context strategies for multi-step agents that preserve state, tool outputs, and intermediate results.

7. 7. Measuring and Optimizing Context Performance

   4 lessons

   Build evaluation frameworks to test whether models actually use the context you provide and improve over time.