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Context Engineering

Definition
AI-generated

Context engineering is the practice of selecting, structuring, and maintaining the information placed in a model’s working context—retrieved passages, tool outputs, memories, structured state, and conversation history—so that the model has the right facts in the right order and format, not only well-written instructions.

Topics
LLM and Agents
Related
Context Context Window Genome-Wide Association Study (GWAS)

Why it matters in GWAS

Large phenotypes, long methods text, and heterogeneous supplementary tables often need chunking, retrieval, de-duplication, and schema-aware assembly before they reach the model; weak context curation causes omissions or contradictions even when prompts are carefully worded.

Example usage

"During downstream analysis, Context Engineering was applied before final reporting of lead findings."

Context Context Window Genome-Wide Association Study (GWAS) In-Context Learning Large Language Model (LLM) Prompt Engineering Retrieval-Augmented Generation (RAG)
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Lewis P, et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. NeurIPS.
  • Liu N, et al. (2023). Lost in the middle: how language models use long contexts. arXiv:2307.03172.
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