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Compression and Contextual Distillation (ANSWERED)

Scenario BasedRAGMedium15 min read

Medium RAG interview question on compression and contextual distillation — architecture, trade-offs, eval, and production patterns.

TL;DR — Quick Answer

Compress retrieved chunks via extractive summarization, LLM distillation, or sentence selection before generation. Cuts token cost and distraction but can drop critical details — validate faithfulness after compression.

The Interview Question

Explain compression and contextual distillation. How would you design, implement, and evaluate this in a production RAG system? Discuss trade-offs and failure modes.

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