· Generative AI · 2 min read
Debugging AI Hallucinations: Why Agents Lie and How to Ground Them
Agents don't just 'make things up'. They suffer from retrieval failures and context noise. We analyse the anatomy of a hallucination and how to fix it with RAG and citations.
An AI agent does not “lie” in the human sense. It is simply a probabilistic engine completing a pattern. When that pattern diverges from reality, we call it a hallucination. In enterprise systems, this isn’t a quirk: it is a critical failure.
To fix hallucinations, we must first understand their mechanical source.
The Three Types of Retrieval Failures
Most “creative” errors in RAG (Retrieval Augmented Generation) systems stem from three specific points in the data pipeline:
- The Missing Context: Use of a vector database allows us to find similar text, but similarity does not equal relevance. If the answer isn’t in the top 3 chunks, the model will try to fill the gap with its pre-trained knowledge.
- Stale Data: An agent might retrieve an “current” policy document that was actually superseded last month. Without metadata filtering for dates, truth becomes ambiguous.
- Context Poisoning: Feeding the model too much irrelevant data confuses the attention mechanism. It grabs a detail from Document A and applies it to the question about Document B.
Engineering the Truth: Grounding Techniques
We fix this through Grounding. This means forcing the model to cite its sources and refusing to answer if no source is found.
1. Citation Enforcement
Do not ask for an answer. Ask for an answer with a reference ID. System Prompt: “You must cite the Document ID for every claim. If you cannot find the answer in the context, output ‘I do not know’. Do not guess.”
2. Fact-Check Agents
Deploy a secondary, smaller agent (like Haiku or Gemini Flash) to audit the primary response. Prompt: “Verify if the claim in the response is supported by the provided text. If not, flag as hallucination.”
3. Metadata Filtering
Never search blindly. If a user asks about “2024 HR Policies”, your vector search must apply a strict date filter: date >= 2024-01-01.
Conclusion
Reliability is not magic. It is engineering. By treating hallucinations as a data pipeline bug rather than a model mystery, we can build agents that enterprises can actually trust.
Why Alps Agility?
We build grounding systems that keep your AI honest. From strict RAG pipelines to automated hallucination detection, we turn experimental bots into reliable business tools.
Contact us today to audit your AI reliability.
Reference: Google Cloud: Grounding with Vertex AI
