5 Query Translation Tips To Boost RAG Performance

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How to get near-perfect LLM performance even with ambiguous user inputs

Query translation techniques like multi-query, RAG-fusion, decomposition, step-back prompting, and HyDE greatly improves the performance of RAG based LLM apps.
Photo by travelnow.or.crylater on Unsplash

You can’t be more wrong than assuming the user would ask the LLM the perfect questions. Rather than directly executing, what if we refine the user’s problem? That’s query translation.

We built an app that lets users query through all the documents my company ever produced. These include PPTs, project proposals, progress updates, deliverables, documentation, etc. It was remarkable because many such attempts in the past fell short. Thanks to RAGs, this time, it was very promising.

We did a demo, and everyone was excited to use it. The initial rollout was for a small, selected batch of staff. But what we noticed wasn’t very exciting to us.

This was expected to be a game-changer in the way we work. But most users tried the app only a few times and never used it later. They quit the app as if it were a toy project for school kids.

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