Skip to content
Nitmonk
RAG & Retrieval (Enterprise)

Where Hallucinations Come From

Failures across the query, retrieval and model layers.

Where Hallucinations Come From — infographic explaining Failures across the query, retrieval and model layers.
Where Hallucinations Come From — visual explainer by Nitmonk.

Hallucinations are not just a model problem — they come from the entire pipeline. This traces where hallucinations originate across the query, retrieval and model layers of a RAG system.

In simple terms

Hallucinations come from failures at every layer, not just the LLM.

How it works

  1. 1Query layer: ambiguous queries, poor prompts and missing context cause wrong intent.
  2. 2Retrieval layer: wrong or missing documents, bad chunking and low recall starve the model of facts.
  3. 3LLM layer: guessing, mixing facts and missing citations produce wrong answers.
  4. 4Each weak layer compounds into a wrong final answer.
  5. 5The goal: a robust pipeline that reduces failures at every layer.

Key points

  • Prompt failures: unclear or incomplete queries lead to wrong intent.
  • Retrieval failures: the system fetches the wrong or missing information.
  • Context failures: poor chunking or noisy data weakens the model's context.
  • Model & output failures: guessing plus no verification lets wrong answers through.

Why it matters

If you treat hallucination as only a model issue, you'll never fix it. Understanding its pipeline-wide origins lets you reduce failures at each layer.

Frequently asked questions

Are hallucinations only the model's fault?
No — ambiguous queries, bad retrieval and poor context all contribute before the model even generates.
How do I reduce them?
Improve the query, retrieval and context layers, then add citations, verification and guardrails at the output.

More in RAG & Retrieval (Enterprise)

RAG Retrieval Optimization Layer

Retrieve the right context with high recall and relevance.

RAG: Retrieval Re-ranking

Re-rank retrieved docs to keep only the most relevant.

RAG Optimization 1 · Hybrid Search

Keyword + semantic search for higher recall.

RAG Optimization 2 · Re-ranking

Find the most relevant docs before the LLM.

RAG Optimization 3 · Context Pruning

Remove noise; keep only what matters.

RAG Optimization 4 · Semantic Cache

Stop asking the same question twice.

Back to all AI concepts