TITLE: How do I reduce hallucinations in LLMs using Retrieval-Augmented Generation (RAG)?

All answers to this question.

To implement RAG effectively, you need to move away from relying solely on the model's weights. First, convert your PDF and markdown files into "chunks" using a library like LangChain. These chunks are then converted into numerical vectors using an embedding model (like OpenAI’s text-embedding-3) and stored in a vector database such as Pinecone or Milvus. When a user asks a question, your system performs a semantic search to find the most relevant chunks and passes them to the LLM as "context." This forces the model to stay "grounded" in your specific documents, which significantly drops the hallucination rate.

   Answered 2024-05-16 by Emily Richardson

What strategy are you using for your "chunk size"? If your chunks are too small, the model loses context; if they are too large, you might exceed the token limit or introduce irrelevant noise into the prompt.

   Answered 2024-05-17 by Michael Stevens

• Michael, I'm currently using a fixed 500-character chunk with a 50-character overlap. After testing, I found that "semantic chunking"—where you break text based on sentence meaning rather than character count—works much better for technical manuals. I’ve also implemented a "re-ranking" step using Cohere to make sure the top 3 results are truly the most relevant before the LLM sees them. This combination has lowered our error rate from roughly 20% down to under 2%.

     Commented 2024-05-19 by David Miller

You should also check your "temperature" setting. Keeping it near 0.0 or 0.1 for factual tasks ensures the model stays more deterministic and less "creative" with your private data.

   Answered 2024-05-20 by Sarah Jenkins

• Sarah is spot on. Lowering the temperature is the quickest win for RAG systems. I usually set mine to 0.0 for any internal knowledge base to ensure the answers remain as literal as possible.

     Commented 2024-05-21 by Emily Richardson