How Context Compression and Prompt Design Make RAG Systems Production-Ready

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Part 4 of a developer series on Retrieval-Augmented Generation (RAG) systems shifts focus from retrieval to the generation stage of the pipeline. The article explains that even accurate retrieval fails if the language model is handed too much noisy or redundant context, making compression a critical step rather than an optional one. It outlines how reducing retrieved chunks to only the most relevant sentences lowers token costs, cuts latency, and reduces the risk of hallucinations. The piece also covers prompt construction techniques that keep model outputs grounded and consistent, particularly when retrieved information is incomplete. Finally, it introduces evaluation frameworks for measuring faithfulness, relevancy, precision, recall, latency, and cost in live production environments.

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