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A RAG app can run without producing a useful answer. The failure may be in retrieval—the evidence brought back is missing, irrelevant, or buried—or in generation, where the model receives suitable evidence but fails to use it. Debug those stages separately before changing prompts, chunk sizes, embeddings, or models.
Start with a failed question, not a guessed fix
Choose a small set of real questions that your system answered incorrectly, vaguely, or without support. For each, save the query, the expected answer or supporting evidence when available, the retrieved passages, and the final response. This gives you a repeatable test set instead of relying on a successful demo or a vague impression that the system has improved.
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RAG introduces an additional axis to optimize: the model’s response depends on what retrieval supplies as well as on how the model uses it. A changed prompt cannot recover evidence that was never retrieved, and better retrieval alone cannot ensure that the answer follows its context. OpenAI’s optimization guide recommends evaluating what went wrong before taking an optimization step.
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Run the retrieval stage by itself for each saved question. Inspect the returned chunks, their source metadata, and their order. Ask whether the expected evidence appears among the results and whether the most useful passage is prominent enough to reach the model.
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- Expected evidence is absent: investigate document ingestion and parsing, chunk boundaries, filters, query construction, and retrieval settings. These are candidate areas to inspect, not a diagnosis of any particular stack.
- Evidence is present but buried: check the ranking and the amount of irrelevant or redundant material returned alongside it.
- Passages conflict or look incomplete: inspect the original source and how it was parsed or split before changing the response prompt.
Retrieval evaluation can compare returned documents with expected document IDs when you have labeled examples. The LlamaIndex evaluation documentation describes retrieval and response evaluation as distinct tasks.
Inspect the exact context the model received
If useful evidence was retrieved, examine the actual context passed to the language model—not just the search results displayed by a separate retrieval test. Check whether the relevant passage was truncated, diluted by noise, or contradicted by another passage. Also inspect the instructions supplied with the context: does the model know to ground claims in it and to say when the evidence is insufficient?
Even good retrieval does not guarantee a grounded response. A model may receive relevant material and use it incorrectly; it may also be misled by irrelevant or incorrect material. OpenAI’s PDF parsing cookbook illustrates why document processing matters to what a RAG system can retrieve and use.
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Compare each response with both the original question and the context actually supplied. Keep these checks distinct: a response can be relevant to the question but unsupported, or faithful to the context while failing to answer what was asked.
- Retrieval hit or relevance: did the results include the expected evidence, and was it useful?
- Context relevancy: did the retrieved material help answer the query, or did it add distracting noise?
- Faithfulness: are the answer’s claims supported by the supplied context?
- Answer relevance: does the response address the user’s actual question?
- Correctness: does it match a trusted reference answer, when you have one?
These measures are not interchangeable. The LlamaIndex evaluation concepts distinguish retrieval evaluation from response evaluation, including faithfulness, context relevancy, answer relevance, and correctness against a reference. Reference-based correctness requires labeled answers; faithfulness alone does not establish that the context itself is true.
Change one component and rerun the same cases
- Keep the failed questions and their expected evidence or reference answers as a small regression set.
- Use the stage checks above to identify the most likely failure area before editing the system.
- Change one relevant component at a time, such as parsing, retrieval configuration, or response instructions.
- Rerun the same questions and compare retrieval and response behavior across the diagnostic dimensions.
- Keep the change only if it improves the relevant failures without introducing new ones in the same examples.
The exact instrumentation and metrics depend on your implementation. The point is not to assume that a particular chunk size, framework, or retrieval strategy is universally best; it is to make each change answer a testable diagnosis. OpenAI’s guide summarizes the principle: “The key thing to take away here is that the principle remains the same from our mental model at the beginning – you evaluate to find out what has gone wrong, and take an optimization step to fix it.”
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This sequence narrows a bad answer to a stage and gives you a way to check whether a change helped. It cannot identify the root cause of an unseen implementation: the relevant details depend on your framework, vector store, embedding model, document formats, and deployment. RAG also does not guarantee hallucination-free output; retrieved context can be wrong or noisy, and a model can mishandle suitable evidence.
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