Your RAG is blindly trusting your vector database. Here is how we fix it.

If you’ve built a basic RAG pipeline, you know the ugly truth: you ask for the nearest documents, the system finds them, and hands them to the model as fact. The problem? "Nearest" is not the same as "right". A document can share half its vocabulary with your query, rank at the top, and answer a completely different question.

At Scorvia Studio, we build robust AI pipelines for businesses that simply cannot afford hallucinations. We just put together an interactive demo showing how we implement Corrective RAG (CRAG) to solve this exact issue: https://scorvia.studio/demos/crag/

The idea (originating from the 2024 paper by Yan et al.) is straightforward: we put a grader between the retrieval and the generator. Nothing reaches the generator unread.

Here is what the flow looks like in our implementation:

1. The Grader
   Instead of a standard LLM prompt, we use a fine-tuned T5-large model as a grader. It assigns a relevance score from -1 to +1 to the retrieved documents. It’s not a simple binary yes/no.

2. The Thresholds
   We set upper and lower thresholds that are tuned specifically per dataset (a demo claiming a universal "0.7 threshold" is misreading the paper).

Correct (above upper): At least one document is good. We use the local corpus.

Incorrect (below lower): The corpus is useless. We rewrite the query and do a web search.

Ambiguous (in the middle): We combine both corpus and web.

1. Decompose, then Recompose This is where the magic happens. Even a document that passes the threshold is still mostly noise. We cut it into strips, score each strip individually, drop the weak ones, and join the survivors back together in their original order. The generator only reads the exact sentences that actually earned their place.

The honest tradeoff:
Precision isn't free. Latency goes from roughly 400 ms to about 750 ms, and a miss adds a search round-trip on top of that. You are now making two LLM calls per query instead of one. Widening the retrieval also widens your grading bill, because the grader has to run once per document.

What you buy for that price:
Hallucinations drop to near zero, and your answers can confidently include facts published long after your index was built. What keeps CRAG in production for our clients is that it bolts seamlessly onto an existing pipeline without needing to touch or fine-tune the generator model (unlike Self-RAG).

For serious, high-stakes products, this trade is 100% worth making.

If you are a founder or an enterprise looking to build a premium AI product where accuracy isn't optional, stop settling for toy AI wrappers. At Scorvia Studio, we build retrieval systems that actually check themselves. Reach out to us, and let's get your pipeline ready for production.