context-compressionlisted

# Context compression Retrieval returns passages that are relevant to the query; much of each passage is not relevant to the answer. Compression removes the excess before it consumes context, which improves both cost and the model's attention on what matters. ## Method 1. **Filter before compressing.** Dropping a passage that should not have been retrieved is cheaper and safer than summarising it (see retrieval-reranking). 2. **Extract the relevant spans.** Pulling the sentences that bear on the question preserves exact wording, which matters for citation and for faithfulness (see citation-grounding). 3. **Prefer extraction to summarisation.** Summarising introduces a paraphrase step that can distort, while extraction cannot invent. 4. **Compress per passage, not across them.** Merging passages loses provenance and blends sources that should stay distinguishable. 5. **Keep the structure that carries meaning.** Headings, dates, and identifiers are small and disproportionately useful for the model's reasoning. 6. **Budget explicitly.** Decide how much context the answer step gets and compress to fit, rather than sending everything and letting the window truncate arbitrarily (see context-window-management). 7. **Measure the effect on answer quality.** Compression that improves cost and degrades faithfulness is not a win (see rag-evaluation). ## Boundaries Compression trades completeness for focus, and an aggressive filter can remove the pass