Why Your RAG Chatbot Still Hallucinates: 5 Design Mistakes (and How to Fix Them)
You built a RAG chatbot. The documents are indexed, the vector database is humming, and the demo went well. Then a user asks about the refund policy, and the bot confidently quotes a rule that was retired last year. RAG is supposed to reduce hallucinations. It does, but only when retrieval is good. The pipeline has two steps: find the relevant text, then generate an answer from it. If the text…
Your RAG chatbot may still produce inaccurate information, or "hallucinations", due to design issues in the system. The process of generating a response involves two main steps: finding the relevant text and generating an answer from it. If the retrieved text is incorrect, incomplete, or outdated, the model will generate a confident but wrong answer. Five common design mistakes contribute to this issue:
1. Splitting document chunks in a way that cuts off essential information. Fixed-length chunks often break up key details, such as definitions and lists. To fix this, split chunks based on structure, keep a small overlap between chunks, and include document metadata like titles and headings with each chunk.
2. Relying solely on vector search for retrieval. While embeddings are good at understanding meaning, they struggle with exact matches like product codes and error IDs. Use a combination of keyword search (like BM25) and vector search, then re-rank the top candidates to select the best ones for the model to generate a response from.
3. Stale and conflicting data in the index. Documents change over time, but the index may not reflect these updates. Multiple versions of a policy could coexist in the index, causing confusion for the retrieval system. Store metadata with each chunk, re-index documents upon changes, remove or filter out outdated versions, and display the source and its date in the answer.
4. Lack of evaluation metrics. Simply observing whether the chatbot works feels inadequate. Without proper measurement, it's impossible to determine if design changes are effective. Create a test set of real questions with known correct answers and measure retrieval accuracy and generation quality separately. Re-run the test after any adjustments.
5. Lack of a "I don't know" response. Language models tend to generate plausible answers even when the context doesn't contain the information. To mitigate this, instruct the model to only use the provided context, give it an explicit way to decline when the answer isn't available, require citations for any claims, and set a retrieval score threshold below which the model should decline or ask for clarification. Log declined questions to identify gaps in the documentation.
When a wrong answer is encountered, check these steps in order: was the relevant information present in the index? Was it retrieved? Did it rank high enough to be included in the prompt? Did the model actually use it? Most issues arise during the first three stages, so focus on improving retrieval and context usage before blaming the model. The RAG and Vector Databases guide on ScaleDojo provides a detailed architecture overview for debugging and optimizing these components.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
