Prompt compression for RAG

Reduce retrieved context without hiding the trade-off.

Protect the facts and structures your application depends on, compress low-value context, inspect the redline, and evaluate answers on representative retrieval workloads.

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✓Protected citations and identifiers

✓Original-input fallback

✓Inspectable before and after

✓Workload-specific evaluation

Protect

Define what must survive.

RAG quality depends on more than semantic similarity. Mark the details that must remain exact.

Numbers, units, dates, and identifiers
Citations, source labels, and tenant terms
Instructions, negations, and output constraints

Compress

Remove low-value context selectively.

Use compression when dynamic retrieval context is long enough to justify the added work.

Skip prompts that are already efficient
Preserve structure and critical spans
Return the original when savings are not worthwhile

Verify

Measure the downstream task.

A smaller prompt is not a win if the answer becomes less grounded, complete, or useful.

Compare original and compressed outputs
Score grounding and information preservation separately
Review failures on a frozen representative sample

Good fit

Dynamic context with visible input cost and a measurable task.

Document QA, research synthesis, support knowledge, compliance review, and other retrieval-heavy workflows are strongest candidates when prompt size is material and the team can define a correct downstream result.

Test compression on the RAG workload you actually run.

Start with representative prompts, explicit must-keep information, and the exact model configuration used in production.

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