# Hallucinations and How to Ground an LLM

> Why LLMs state false things confidently, and the practical techniques that reduce it: grounding, citations, constrained outputs and checks.

- Source: https://akshaythakare.com/notes/generative-ai/hallucinations-and-grounding
- Topic: Generative AI
- Level: intermediate
- Reading time: 2 min
- Tags: genai, llm, hallucination, rag, interview
- Updated: 2026-10-11

## Why it happens

An LLM generates text that is likely given its training and its prompt. It has no built-in check against a source of truth. When it lacks the information, a fluent but wrong answer can still be likely text.

Common triggers:
- questions about facts not in the prompt (recent events, private company data)
- precise details: numbers, dates, names, citations
- prompts that pressure for an answer ("you must answer")

## Techniques that help

| Technique | How |
| --- | --- |
| **Grounding (RAG)** | Put the relevant source text in the prompt and instruct the model to answer from it |
| **Allow "I don't know"** | Say explicitly that "not found in the documents" is a valid answer |
| **Citations** | Ask for the source passage id for each claim, then verify the ids exist |
| **Structured output** | Constrain answers to a schema or a fixed set of labels |
| **Tools for facts** | Let the model call a database or calculator instead of recalling numbers |
| **Verification step** | A second check: does every claim appear in the sources? |

```text
Answer using only the context below. If the answer is not in the context,
reply exactly: "Not found in the provided documents."
Cite the passage id for each statement, like [doc-3].
```

## Measure it

Build an evaluation set with questions whose correct answers (and "not in the documents" cases) are known, and track groundedness over time.

> **Warning: Lower is not zero**
>
> These techniques reduce hallucinations; they do not eliminate them. High-stakes outputs need human review or automated verification.