"Context engineering has emerged as one of the most critical skills in working with large language models (LLMs). While much attention has been paid to prompt engineering, the art and science of managing context-i.e., the information the model has access to when generating responses-often determines the difference between mediocre and exceptional AI applications. After years of building with LLMs, we've learned that context isn't just about stuffing as much information as possible into a prompt."
"Modern LLMs operate with context windows ranging from 8K to 200K+ tokens, with some models claiming even larger windows. However, several technical realities shape how we should think about context: Lost in the middle effect: Research has consistently shown that LLMs experience attention degradation in the middle portions of long contexts. Models perform best with information placed at the beginning or end of the context window. This isn't a bug. It's an artifact of how transformer architectures process sequences."
Context engineering is a critical skill for working with large language models. Managing the information the model can access often determines application quality more than prompt wording alone. Context design should prioritize strategic information architecture rather than maximizing token quantity. Modern models offer context windows from about 8K to over 200K tokens, but effective fidelity drops beyond roughly 32K–64K tokens and attention degrades in middle positions. Information placed at the beginning or end of context performs best. Very long contexts increase latency and compute costs nonlinearly, potentially making larger windows impractical without selective retrieval and careful placement.
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