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Context

Definition
AI-generated

In large language model systems, context is the information the model can attend to in a single inference—typically the concatenation (or structured packing) of system instructions, user input, retrieved passages, tool outputs, and (in chat setups) prior turns.

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

Why it matters in GWAS

Logging and versioning the full context—not only the final user question—matters for reproducibility when LLMs summarize methods, harmonize traits, or validate summary-statistic columns; retrieval and chunking choices change the context and therefore the answer.

Example usage

"We diffed the two runs and found the mismatch was extra PubMed paragraphs in the second context, not a model upgrade."

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

  • Brown T, et al. (2020). Language models are few-shot learners. NeurIPS.
  • Vaswani A, et al. (2017). Attention is all you need. NeurIPS.
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