I am currently developing a support bot for my company, but we are seeing frequent factual errors in the responses. Are there specific prompting techniques or grounding methods that work best for generative AI models to ensure they stay within our knowledge base? I need a solution that balances conversational flow with strict data accuracy to avoid misleading our clients.
3 answers
The most effective way to curb hallucinations is implementing Retrieval-Augmented Generation (RAG). Instead of relying on the internal knowledge of generative AI models, you should feed the model relevant snippets from your verified documentation before it generates a response. Additionally, use a system prompt that explicitly tells the model to say "I don't know" if the answer isn't in the provided context. We saw a 40% increase in accuracy once we started using vector databases like Pinecone to manage our company data. This ensures the output is grounded in reality rather than statistical probability.
Are you using a temperature setting of 0.0 to ensure deterministic outputs, or are you letting the generative AI models be creative with their responses?
You should definitely look into "Chain of Thought" prompting; it forces generative AI models to reason through the steps before giving a final answer.
Mark is spot on. Forcing the model to explain its logic step-by-step often catches errors that a direct answer would miss entirely.
I’ve kept the temperature at 0.7 for a more "human" feel, but based on your question, it seems that lowering it significantly might be the first step to fixing the logic errors.