It seems like every week a new startup launches a tool specifically for "RAG evaluation." Are most RAG systems so prone to failure that they need their own separate observability category away from general machine learning models?
3 answers
The reason is that RAG adds a whole new layer of complexity: the retrieval engine. In a standard model, you just monitor inputs and outputs. In a RAG setup, you have to monitor the "faithfulness" of the answer to the context. Many systems are "badly designed" because they assume the retriever is always right. These new tools use "LLM-as-a-judge" patterns to score the relationship between the prompt, the retrieved chunks, and the generated response. It's a three-way verification that general monitoring tools simply weren't built to handle, making this specialization necessary for accuracy.
Scott here. Is the latency introduced by these "judge" models significant enough to affect the user experience in a live application?
I think we're seeing this boom because "groundedness" is the only thing standing between a useful tool and a hallucination machine.
Rebecca, you're 100% correct. Jeffrey, the focus is shifting from "how fast is the AI" to "how correct is the AI," and that requires a new breed of observability.
Scott, most teams run these evaluations asynchronously. You log the interaction, and the judge model scores it in the background. This way, you get the data you need to improve the system without making the user wait. For real-time safety, you use lighter "guardrail" models that are much faster but less detailed than a full LLM judge.