How to Stop Hallucinations in RAG Chatbots: A Complete Guide

Hallucinations in RAG (Retrieval-Augmented Generation) chatbots can undermine user trust and lead to misinformation. In this comprehensive guide, we’ll explore proven strategies to minimize these AI-generated inaccuracies and build more reliable chatbot systems.

If you’re building a RAG chatbot, you’ve likely encountered the frustrating problem of hallucinations—when your AI confidently provides incorrect or fabricated information. The good news? There are effective, battle-tested solutions to dramatically reduce these errors. Let’s dive into the multi-layered approach that actually works.

Retrieval Quality Improvements

The foundation of any reliable RAG system is high-quality retrieval. If your chatbot can’t find the right information, it’s much more likely to make things up. Here’s how to get retrieval right:

Better Chunk Strategy

One of the most impactful changes you can make is optimizing how you chunk your documents. Use smaller, semantically coherent chunks—typically between 150-300 tokens—with some overlap between chunks. This sweet spot ensures each piece contains enough context while remaining focused.

Don’t forget to include metadata like source, date, and context information. This helps the model assess relevance and gives users transparency about where information comes from.

Hybrid Search

Relying on a single search method leaves gaps. Combining dense embeddings (semantic search) with sparse retrieval methods like keyword search or BM25 gives you the best of both worlds. Semantic search catches conceptual matches, while keyword search ensures you don’t miss exact terms and phrases.

Reranking

After initial retrieval, add a reranking step using a cross-encoder model to surface the most relevant passages. Models like Cohere’s reranker or BGE-reranker can significantly improve the quality of context sent to your LLM, which directly reduces hallucinations.

Query Reformulation

Sometimes the user’s question isn’t phrased in a way that matches your documents. Use your LLM to generate multiple query variations or hypothetical answers, then retrieve for each variation. This comprehensive approach ensures you’re not missing relevant information due to phrasing differences.

Generation Controls

Even with perfect retrieval, you need to guide your LLM to use that information correctly. These generation controls act as guardrails:

Explicit Grounding Instructions

Your prompts should explicitly instruct the model to only answer from the provided context. More importantly, teach it to say “I don’t have information about that” when the context is insufficient. This simple instruction can prevent countless hallucinations.

Citation Requirements

Force your model to cite specific passages for each claim it makes. This serves two purposes: it makes hallucination harder (since the model must ground each statement), and it makes verification easier for both you and your users.

Confidence Scoring

Have your model rate its confidence in each response, or flag when it’s uncertain. You can implement this through explicit prompting or by analyzing the model’s logprobs (log probabilities). Low confidence responses can trigger additional verification or simply be flagged to users.

Structured Outputs

Use JSON mode or structured generation formats to separate the actual answer from metadata like confidence levels and source citations. This makes it easier to programmatically verify responses and handle uncertain answers appropriately.

Post-Processing Verification

Don’t just trust the model’s output—verify it programmatically:

Entailment Checking

Use a Natural Language Inference (NLI) model to verify that claims in the response are actually entailed by the retrieved context. This automated fact-checking step catches many hallucinations before they reach users.

Self-Consistency

Generate multiple responses to the same query and check for agreement. Alternatively, have the model review its own answer against the source documents. Inconsistencies are red flags for potential hallucinations.

Fact Extraction and Verification

Extract specific factual claims from the response and verify each one against your source documents. This granular approach catches subtle inaccuracies that might slip through other verification methods.

System Design Best Practices

Beyond individual techniques, how you design your overall system matters:

Context Window Management

More isn’t always better. Don’t overflow the context window with too many retrieved documents. In practice, 3-5 highly relevant chunks often outperform 20 mediocre ones. Quality over quantity is the rule here.

Retrieval Feedback

Show users the sources your chatbot used and provide a way for them to report when responses don’t match those sources. This feedback loop helps you continuously improve your system and builds user trust.

Fallback to Retrieval

If your retrieval system finds no good matches (indicated by low similarity scores), don’t let the model generate an answer anyway. Instead, simply inform the user that no relevant information was found. It’s better to be honest about limitations than to hallucinate.

Fine-Tuning

Consider fine-tuning your embedding model on domain-specific data to improve retrieval accuracy. You can also fine-tune your LLM to better follow grounding instructions specific to your use case.