Fat Context of RAG Drives Inference Cost Sky-high. Here’s How to Save Big on API Calls.

Author(s): Thuwarakesh Murallie

Originally published on Towards AI.

Try this before you dump your RAG prototype.

What prevents you from deploying that RAG app you developed?

The article discusses how to mitigate high inference costs in Retrieval-Augmented Generation (RAG) applications by employing context compression techniques. It emphasizes that developers do not need to abandon their RAG projects due to API costs, as there are effective strategies to maintain response quality while significantly reducing input tokens. Specifically, it introduces two compression methods from recent research: extractive and abstractive compression, which aim to selectively condense context without losing essential information, ultimately leading to cost savings and improved performance.

Read the full blog for free on Medium.

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Published via Towards AI