LLMs hallucinate because they're trained to predict the next token, not to be factually correct. It's a fundamental capability issue. You can't really 'fix' it by training better. But you can mitigate it by constraining the output space. Retrieval-augmented generation (RAG) is the main approach. Instead of asking the LLM to generate text from nothing, you give it relevant source documents and ask it to synthesize based on those sources. The LLM is constrained: if the sources don't say something, it's harder to hallucinate. Not impossible. It can still hallucinate within or outside the retrieved sources. But probability is lower. Other mitigations: prompting (tell the LLM to cite sources, to say 'I don't know' if unsure). Temperature reduction (lower temperature = more deterministic, fewer wild hallucinations). Fine-tuning on grounded data (train on Q&A pairs where A is grounded in sources). Output filtering (automatically detect hallucination signals and reject outputs). The challenge with RAG-based mitigation is that retrieval isn't perfect. If you retrieve the wrong sources, the LLM still hallucinates, just now it hallucinates based on wrong sources. So retrieval quality matters enormously. Good retrieval + RAG = low hallucination. Bad retrieval + RAG = still hallucinates. There's also the subtlety problem. Obvious hallucinations are easy to catch (makes up a person who doesn't exist). Subtle hallucinations are hard (distorts someone's position slightly, exaggerates statistics). Groundedness evals can catch the obvious ones. Detecting the subtle ones is harder. Synap's hallucination mitigation combines high-quality retrieval, RAG implementation, and groundedness validation to systematically reduce hallucinations in your AI systems.
Why It Matters
Hallucination is the plague of LLMs. A confident, well-written hallucinated answer is worse than a correct answer with caveats. Users trust the confident wrong answer. Hallucination undermines all trust in AI systems. Effective mitigation is table stakes for any production AI application.
Example
A legal AI asked 'what's the precedent for X?' Without mitigation: might hallucinate a court case that doesn't exist. With RAG mitigation: retrieves actual case law, synthesizes answers based on real precedents. Doesn't guarantee it won't hallucinate details, but constrains output to actual legal landscape.