In this article, we’ll look at how LLMs actually process the information you give them, what context engineering is, and the strategies that can help with it.

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Giving LLMs more context can actually hurt performance — a 2025 Chroma study found all 18 tested frontier models degraded as input length grew, with some dropping from 95% to 60% accuracy. This is due to architectural constraints: attention is unevenly distributed (the 'lost in the middle' problem), and 'context rot' causes unpredictable performance cliffs. Context engineering — the practice of designing what goes into the context window — addresses this through four strategies: Write (save info externally and retrieve when needed), Select (use RAG to pull only relevant chunks), Compress (summarize history to reduce token bloat), and Isolate (split work across multiple specialized agents with focused contexts). Each strategy involves tradeoffs between information loss, cost, retrieval precision, and system complexity. Anthropic's multi-agent research system achieved a 90.2% improvement over a single agent purely through better context management.

A Guide to Context Engineering for LLMs

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