I am currently working on a customer-facing chatbot using a pre-trained LLM. However, I am seeing frequent "hallucinations" where the AI provides factually incorrect data about our specific pricing. What are the best industry-standard techniques to ground these generative models using our internal corporate database to ensure high accuracy and reliability?
3 answers
The most effective way to tackle this is by implementing Retrieval-Augmented Generation (RAG). Instead of relying solely on the model's internal weights, RAG allows the system to query your specific vector database first. By retrieving relevant document chunks and feeding them into the prompt as context, you ground the response in "truth." We’ve seen a massive shift toward this in 2024 because it’s much cheaper than full fine-tuning. Additionally, you should implement strict system prompts and temperature settings (close to 0) to limit the model's creative liberty during the generation phase.
Have you considered if your current chunking strategy for the vector database is too broad? Sometimes the "noise" in retrieved documents causes the LLM to get confused.
Fine-tuning on a small, high-quality dataset of Q&A pairs can also help the model learn the specific "tone" and "format" of your brand's responses.
I agree with Laura. While RAG provides the facts, fine-tuning helps with the domain-specific vocabulary that generic models often miss.
Justin makes a great point. If your overlap between chunks is too small, the model loses context. We found that using a recursive character splitter with a 10% overlap significantly improved our retrieval precision. Also, try adding a "re-ranking" step after the initial search to ensure the most relevant pricing data is at the top of the context window.