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Have you ever ever noticed that paperwork retrieved by RAG programs might not all the time align with the person’s question?
This can be a widespread incidence, significantly with off-the-shelf RAG implementations. Paperwork might lack full solutions to the question, comprise redundant info, or embrace irrelevant particulars. Moreover, the order through which these paperwork are offered might not constantly match the person’s intent.
On this put up, we’ll discover three efficient strategies to reinforce doc retrieval in RAG-based functions:
- Question enlargement
- Cross-encoder re-ranking
- Embedding adaptors
By incorporating these strategies, you may retrieve extra pertinent paperwork that carefully match the person’s question, thereby growing the affect of the generated reply.
Let’s take a look 👇.
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Question enlargement refers to a set of strategies that rephrase the unique question.
Two standard strategies which might be simple to implement will probably be mentioned on this article.
👉 Question enlargement with a generated reply
Given an enter question, this methodology first instructs an LLM to offer a hypothetical reply, no matter its correctness.
Then, the question and the generated reply are mixed in a immediate and despatched to the retrieval system.
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