Speculative and Corrective RAG
Plain RAG blindly trusts whatever it retrieves. Corrective RAG adds a quality gate that grades the documents (use them, decompose them, or throw them away and search elsewhere). Speculative RAG attacks the other problem — slowness — by fetching likely contexts in parallel before the router decides. This topic covers both, plus Self-RAG and the metrics that keep them honest.
01.The Problem: Your Search Results Are Trusted Too Much
Here is how "naive RAG" works, in three steps:
- User asks a question.
- Your system searches its documents and grabs the top 5 chunks.
- It pastes those chunks into a prompt: "here is the context, answer using it."
Now imagine step 2 returns mostly garbage — two useful chunks, three irrelevant ones — or returns nothing at all for a question your documents cannot answer.
The model does the thing it was told to do: it trusts you. It cites the wrong chunk. Or it answers from memory and pretends the chunk supported it.
So the question becomes
Where in the pipeline is there a place to say "this context is not good enough"?
Naive RAG has no such place. Two 2023-2024 papers created one:
- Corrective RAG (CRAG): put a quality gate in the middle — grade the retrieved documents, and route to use, decompose, or discard and fall back.
- Speculative RAG: fix the other complaint — slowness — by guessing which retrievals you will need and running them in parallel while the "official" steps decide.
One analogy for both, carried all the way: a careful kitchen.
The chef tastes every ingredient before cooking (the retrieval evaluator), swaps bad produce at the neighbour's shop if needed (web fallback), chops the most likely order while the ticket is still being written (speculative prefetch), and checks the plate before it leaves (groundedness check).
Adaptive RAG is just that kitchen discipline, applied to documents.
Adaptive Retrieval: Judge the Docs, Then Speculate 🔮
Adaptive Retrieval: Judge the Docs, Then Speculate 🔮
Corrective RAG adds a quality gate and fallbacks in the middle of the pipeline; speculative RAG removes latency by fetching probable contexts before the router has decided.
Unlock Topic #266: Speculative and Corrective RAG
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