How I'd Debug a Hallucinating RAG System: A Philips AI Engineer Interview Question
Your RAG system gives hallucinated answers. Where would you debug first?
There are actually quite a few places where things could have gone wrong before the information reached the LLM.
So I would start by tracing what happened to between the user query and what information eventually made it into the context.
→ Did the system call the right tools?
→ Any metadata filtering issues?
→ Anything wrong with query rewriting, for example adding the wrong time constraints?
→ Did retrieval actually fetch the right chunks?
→ Is the chunk quality good? What is your chunking strategy, and how are you evaluating chunk quality (e. using chunk attribution)?
→ Do you retrieve all relevant chunks? Look at 𝐫𝐞𝐜𝐚𝐥𝐥: 𝘰𝘶𝘵 𝘰𝘧 𝘢𝘭𝘭 𝘤𝘩𝘶𝘯𝘬𝘴 𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵 𝘵𝘰 𝘵𝘩𝘦 𝘲𝘶𝘦𝘳𝘺, 𝘸𝘩𝘢𝘵 𝘱𝘦𝘳𝘤𝘦𝘯𝘵𝘢𝘨𝘦 𝘪𝘴 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘦𝘥?
→ Did we retrieve too much irrelevant context? Look at context 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧: 𝘰𝘶𝘵 𝘰𝘧 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘴 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘦𝘥, 𝘸𝘩𝘢𝘵 𝘱𝘦𝘳𝘤𝘦𝘯𝘵𝘢𝘨𝘦 𝘪𝘴 𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵 𝘵𝘰 𝘵𝘩𝘦 𝘲𝘶𝘦𝘳𝘺?
Now suppose all of that is working. We get the right chunks, but the answer is still wrong.
Then the debugging path changes.
→ Was there conflicting information, for example information from different timelines with completely different views?
→ Were the prompt instructions clear?
→ How are we constructing the context? Does the metadata stay together with the chunk?
If you just dump chunks together, you may not give the LLM enough context to understand what that text represents. Things like source, timestamp, or other metadata can give the LLM a better world view of the retrieved information.
The solution depends on the failure mode.
If user queries are ambiguous or underspecified:
→ Maybe query rewriting.
If metadata filtering is wrong:
→ Check the filtering logic.
If retrieval misses exact terms used in the user query:
→ Hybrid search may help. Although it is often already the default.
If retrieved results are relevant but noisy:
→ Reranking
One interesting suggestion I got from the interviewer was about where you place the most relevant chunks after reranking.
For example, if you retrieve 5 chunks, instead of simply ordering them from most to least relevant, what if you put the most important ones at top #1 and bottom #5? The reasoning was the “lost in the middle” problem.
And this is exactly why observability is non-negotiable.
At this point, we have implemented so many things: guardrails, query rewriting, metadata filtering, retrieval, LLM calls, tool calls, tool arguments etc.
We need end-to-end traces and span-level visibility. If you only see the final answer, you don’t know where things went wrong.
What other RAG failure modes have you run into. Any debugging tip you would share?
This is genuinely solid technical content, not a product or company claim — so the "fact check" here is mostly about whether the RAG debugging methodology and the one specific research claim hold up. They do, and the "lost in the middle" reference is more precisely documented than the post itself indicates.
The interview anecdote itself is unverifiable, as personal experience always is — there's no way to confirm the specific interview happened with Philips exactly as described, but nothing about it reads as implausible, and it doesn't need external verification the way a product claim would.
The debugging framework (tracing query → retrieval → context construction → generation) reflects standard, well-established RAG engineering practice, not something invented for this post. Splitting failures into "wrong chunks retrieved" versus "right chunks retrieved, still wrong answer" is the correct first branch point most practitioners in this space use.
Precision and recall definitions — both stated correctly, textbook-accurate for RAG evaluation. Recall as "what percentage of all relevant chunks were retrieved" and precision as "what percentage of retrieved chunks were actually relevant" are the standard, correct definitions used in retrieval evaluation.
The "lost in the middle" reference is accurate, and actually understated — there's a stronger citable stat available. This is a real, highly-cited 2023 paper from Stanford, UC Berkeley, and Samaya AI. Its actual finding: models show a distinctive U-shaped performance curve — much better at using relevant information at the very beginning (primacy bias) and very end (recency bias) of context, with degraded performance when it's buried in the middle — and in one concrete example, GPT-3.5-Turbo's multi-document QA performance dropped by more than 20% when relevant info was placed centrally, in some cases performing worse than giving the model no documents at all. That specific number would make a strong, citable addition to the post.
The interviewer's specific suggestion — placing the most important reranked chunks at position #1 and #5 rather than simply ordering by relevance — is a documented, standard production mitigation, not a novel or unverified tip. Advanced RAG guidance explicitly recommends exactly this: positioning the highest-ranked documents at the beginning and end of the context window, with lower-ranked documents in the middle, precisely because it works with the model's positional bias instead of against it.
One nuance worth adding for completeness: the "lost in the middle" effect, while well-replicated, isn't universal across every model and context length — some follow-up research (including a 2024 benchmark and 2025 work from MIT explaining the underlying attention-bias mechanism) found the effect is less pronounced or doesn't cleanly replicate in some longer-context or more recent model settings, and can be partially mitigated with techniques like query-aware contextualization or attention calibration. Worth knowing that "always put your best chunks at the start and end" is very good general practice, but the size of the effect can vary by model.
Nothing in this post is inaccurate — if anything, it under-cites research that would make its strongest point (the reranking placement tip) even more convincing.