RAG Failure Lab
Start with a deliberately bad retrieval configuration. Change one engineering decision at a time and see why a RAG system can fail even when every component appears to be working.
RAG reliability is a trade-off between finding enough relevant evidence and keeping irrelevant or excessive context out of the model input.
The starting configuration retrieves too many large chunks into a fixed 8,000-token context budget. Your job is to improve answer quality without treating “more context” as automatically better.
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Saved as an Engine checkpoint when the lab starts.
Why can a RAG system fail when retrieval “works”?
A retrieval pipeline can successfully return documents and still produce a weak answer. The important question is not whether retrieval returned something; it is whether the model received the right evidence, with enough coverage and enough precision, inside a practical context budget.
Chunk size is a representation decision
Larger chunks can preserve surrounding context, but they also pull more unrelated text into each retrieval result. Smaller chunks are easier to match precisely, but can separate facts that need to be understood together. There is no universally correct chunk size, so production systems should evaluate it against representative queries.
Top-K changes recall and noise at the same time
Increasing Top-K often improves the chance that a relevant passage appears somewhere in the retrieved set. It also consumes more tokens and introduces more distractors. A system that only optimizes retrieval recall can therefore make the generation stage worse.
Hybrid retrieval and reranking solve different problems
Hybrid retrieval combines complementary first-stage signals such as semantic and lexical matching. Reranking is a second-stage precision step that reorders a candidate set using a stronger relevance model. They can work together, but each adds complexity and latency.
What is simulated here?
The scores in this lab are deterministic educational metrics, not benchmark results from a live embedding model, vector database, reranker, or LLM. The purpose is to make engineering cause-and-effect repeatable. A later Live Mode can compare these predicted trade-offs with real model behavior.
Common questions
No. Production answer quality depends on both evidence coverage and context precision.
Not necessarily. More available tokens do not make irrelevant evidence useful.
No. It is another retrieval strategy whose value depends on the data, queries, and evaluation set.
It lets learners change one variable at a time and observe repeatable causal effects before introducing model variance.