We keep learning how to harness the power of large language models. We also keep bumping into their limits. The big one is hallucination: when a model generates text that’s wrong, nonsensical, or detached from reality. Here are five techniques I use to catch and reduce hallucinations in LLMs, so you can trust more of what your models produce.
Why LLM hallucinations are a real problem
At best, hallucinations are annoying. You end up combing through every output before you can trust it. At worst, they’re dangerous. A confident, fabricated answer spreads misinformation, surfaces the wrong details, or sets false expectations about what the model can actually do.
Hallucinations tend to cause three specific problems:
- They spread misinformation.
- They surface confidential or incorrect information as if it were fact.
- They inflate expectations about what an LLM can reliably do.
The good news is that a handful of proven techniques cut the risk. Here are the five I reach for.
When AI agents hallucinate, the problem usually isn’t the model. It’s what you fed it.
Agents need to reason over your content: product specs, pricing, policies, documentation. But that content is scattered across CMSs, spreadsheets, wikis, and PDFs. No consistent structure. No reliable source of truth. The agent pulls from whatever it finds, and what it finds is messy.
Garbage in, confabulation out.
Sanity gives agents something better to work with. Content in the Content Lake is structured by schema, queryable through GROQ and APIs, and updated in real time. When an agent asks “what does the enterprise plan include?” it gets a typed, versioned answer from a single source of truth, not a best-guess extraction from a marketing PDF last updated six months ago.
This is what makes the difference between an agent that sounds right and one that actually is right. Structure your content for machines to read, and machines read it correctly. Leave it in unstructured blobs, and you’re debugging hallucinations forever.
Less drift. Less fabrication. More signal. Not because you fine-tuned harder, but because you fixed the data layer.
5 techniques to detect and reduce LLM hallucinations
These methods are simpler than they sound. They’re also the most widely used approaches right now.
1. Log probability
Token probabilities are a strong signal for hallucinations. When a model is uncertain about what it generated, that uncertainty shows up in the numbers. Log probability actually beats the entropy of the top-5 tokens at catching hallucinations, which makes it a cheap, reliable first check.
2. Sentence similarity
Compare the generated text against the input prompt or a trusted reference. When the output drifts far from the source, treat that drift as a red flag. This works best when you have ground-truth data to check against.
3. SelfCheckGPT
Use a second model to check the first. When the second model spots inconsistencies or contradictions, you’ve likely caught a hallucination. It’s a practical way to add a reviewer without putting a human on every request.
4. GPT-4 prompting
Better prompts produce fewer hallucinations. Three moves do most of the work:
- Be precise and detailed. Clear, specific guidance gives the model less room to invent. Fill the gaps yourself so the model stops filling them for you.
- Feed it real context. Give the model the topic, the format you want, and any facts it needs. The right context steers it toward the answer you actually want.
- Augment the prompt and iterate. When a prompt produces a hallucination, tighten it and try again. Pair this with a feedback loop, where you score the outputs and adjust the prompt based on what you see.
These prompting moves help a lot. Stay careful, because none of them are foolproof.
5. G-EVAL
G-EVAL scores a model’s output against a set of predefined criteria or benchmarks. When the output misses those criteria, you get a signal that something’s off. It works well as an automated gate before content ships.
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How I keep hallucinations out of my own AI workflows
The techniques above catch hallucinations after the fact. I’d rather prevent them, and my best defense is grounding. I point my models at a single source of truth, my Obsidian vault, where my context, positioning, and proof all live. When a model reasons over structured, current context, it fabricates far less than when it guesses.
I pair that grounding with a human check on anything that ships. That mix of grounding and review lets me move fast with AI and still trust what goes out the door.
Combine the techniques for a layered defense
These five techniques work best stacked together. Each one catches hallucinations from a different angle, so the combination covers gaps that any single method misses. Applied consistently, they make your model outputs far more reliable.
LLM hallucination FAQs
What is an LLM hallucination?
An LLM hallucination is text a model generates that’s wrong, fabricated, or detached from reality, delivered with the same confidence as a correct answer.
Why do LLMs hallucinate?
Models predict likely text, not verified facts. When they lack the right context or hit the edge of their training, they fill the gap with a plausible guess.
How do you reduce hallucinations in an LLM?
Ground the model in trusted context, write precise prompts, and add a check on the output, whether that’s log probability, a second model, or a human reviewer.
Can you fully prevent LLM hallucinations?
You can’t eliminate them entirely yet. You can cut them sharply by combining grounding, careful prompting, and automated checks, then reviewing anything high-stakes before it ships.
