Hallucinations are often blamed on models. That’s convenient. And often wrong.
The Real Cause
Most hallucinations in RAG systems happen because:
- Retrieval failed — the right documents were never surfaced.
- Context was incomplete — only fragments reached the model.
- The pipeline allowed guessing — no guardrails existed to say “I don’t know.”
Watch Out
Models don’t hallucinate in a vacuum. They hallucinate when the pipeline gives them permission to guess.
The Pipeline Is the Problem
When a model produces a confident but wrong answer, trace the failure backward. You’ll almost always find:
- A chunking strategy that split relevant content across multiple fragments.
- An embedding model that couldn’t distinguish the query’s intent.
- A retrieval step that returned irrelevant or outdated context.
- A prompt that didn’t instruct the model to abstain when unsure.
The Solution
Fix the pipeline — and hallucinations drop dramatically. This means:
- Testing chunking strategies against your actual queries.
- Evaluating retrieval quality before blaming the model.
- Adding explicit uncertainty signals to your prompts.
- Measuring end-to-end accuracy, not just fluency.
The best defense against hallucinations isn’t a better model. It’s a better pipeline.
Published Dec 10, 2024 by Noesia Team
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