What Context window means in practice
The context window is the maximum number of tokens an LLM can process in one inference call, counting both the prompt and the generated output. Modern open-weights models offer 8K to 128K-token windows, with some research models pushing to 1M+. The practical engineering trap is that quality typically degrades as the context fills — the famous "lost in the middle" effect — so a 128K context is not a free pass to dump every relevant document in. Better-engineered RAG with tight retrieval and re-ranking beats long-context prompt-stuffing on most enterprise workloads, both in answer quality and in inference cost.
Related terms
Retrieval-Augmented Generation (RAG) →
A pattern where, instead of relying solely on the LLM's training, the system retrieves relevant documents from a knowledge base and includes them in the prompt as context.
Large Language Model (LLM) →
A transformer-architecture neural network trained on very large text corpora to predict the next token in a sequence, producing fluent natural-language output across a wide range of tasks.
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