Overcoming Hallucinations in RAG-Based Chatbots
Trust is the currency of AI. Learn advanced techniques like HyDE, Re-ranking, and Citations to make your RAG pipelines bulletproof.
A chatbot that lies is worse than no chatbot at all. In enterprise RAG (Retrieval-Augmented Generation), accuracy is everything. "Hallucinations"—where the model confidently asserts false information—are the biggest barrier to adoption. Here is how we solve it.
Advanced RAG Techniques
1. HyDE (Hypothetical Document Embeddings)
Users often ask vague questions. HyDE solves this by first asking the LLM to generate a hypothetical answer to the question, and then using that hallucinated answer to search for real documents. Surprisingly, this often yields better search results than the raw query because the hypothetical answer shares more semantic similarity with the target documents.
2. Re-ranking
Vector search is fast but not always precise. We use a two-step process: first, retrieve the top 50 documents using vector search. Then, use a slower, more accurate "cross-encoder" model to re-rank those 50 documents and select the top 5 most relevant ones for the LLM. This drastically improves the quality of the context.
3. Strict Citation Mode
We force the model to quote its sources directly. If it makes a claim, it must provide a footnote linking to the source document. If it can't find a source, it is instructed to say "I don't know."
We implement these safeguards in every private RAG pipeline we build. Reliability is also a core tenet of Pacibook.com, ensuring that the information and interactions on the platform are trustworthy and secure.