Stop Stuffing Your LLM's Context Window
summary
Claim: bigger context windows are not a magic fix and real failures come from how information is fed. Models today support up to about 1,000,000 tokens (an AI engineering book is ~200,000 tokens) yet cost, attention quadraticity, and context rot cause wrong outputs. Practical fixes include token compaction, summarization, specialized agents and reusable skills, and a context assembler/router that uses an intent classifier, memory retrieval, tool selection and observability to avoid JSON bloat, tool overflow and middle‑loss.
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chapters (10)
- 0:00Context engineering for LLMs
- 1:14Why context is crucial
- 2:38How LLMs use context
- 4:05Attention mechanisms & limits
- 7:15Common context issues
- 9:46Tool overflow & bloat
- 11:12Mitigating context issues
- 12:15Specialization & modular context
- 13:38Context routers & tooling
- 14:42Key takeaways
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