What are the biggest risks to accuracy when implementing a RAG system?

All answers to this question.

The biggest risk to RAG (Concept) accuracy is "Semantic Drift" or poor retrieval quality. If your chunking strategy is bad—like cutting a sentence in half—the model receives context that lacks the necessary nuance to answer correctly. Another major issue is "Irrelevant Context Injection," where the retriever pulls in a document that looks similar but is actually about a different topic. This confuses the LLM and leads it to synthesize a wrong answer based on the provided (but incorrect) context. Success depends entirely on the quality of your retrieval and ranking steps.

   Answered 2025-06-19 by Sharon Wood

Do you find that using "Agentic RAG" where the AI decides how to search helps solve the problem of retrieving too much irrelevant information?

   Answered 2025-06-20 by Patrick Riley

• Yes, Patrick, Agentic workflows allow the model to critique its own search results. If the first retrieval doesn't look right, the agent can re-phrase the query and try again. This "self-correction" loop is one of the best ways to ensure RAG (Concept) delivers the high level of accuracy that enterprises require for production-grade apps.

     Commented 2025-06-21 by Edward Barnes

Sometimes the model ignores the retrieved context and relies on its training data anyway. This "Knowledge Conflict" is a huge headache for developers.

   Answered 2025-06-22 by Cheryl Boyd

• Totally, Cheryl. You have to be very strict in your system prompt to tell the model to ONLY use the provided RAG (Concept) data to minimize that conflict.

     Commented 2025-06-23 by Sharon Wood