Unverified50% confidenceSolutionExact time
A healthy LLM application needs to string user feedback, production monitoring, and automated evaluation into a closed loop: collect feedback signals from production, periodically add failure cases to the test set, and save complete execution traces for failure cases.
1
Sources
50%
Confidence
Long-term
Relevance
10/23/2026
First Seen
Sources
LLM App Getting Worse With Every Change? Build an Effective Evaluation System in Three Steps
redditr/LangChain10/23/2026
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UnverifiedEffective LLM evaluation follows an iterative cycle: evaluate performance, analyze failure cases, expand the evaluation set or adjust prompts, and re-evaluate for comparison78% similarUnverifiedCommon approaches to dealing with silent failures include introducing validation based on business semantics, such as result diversity sampling, cross-query result intersection analysis, and collecting explicit feedback from the user side.75% similarUnverifiedopen-code-review resolves the deduplication problem of the rule engine and the LLM repeatedly reporting the same issue through a confidence scoring mechanism.75% similarUnverifiedUsers can objectively verify whether a model has degraded by building a personal "regression test set"—retaining a fixed set of representative prompts to periodically evaluate model output quality.74% similarUnverifiedFor Forecall users on the Pro or Team plans, the evaluation feature can report metrics such as model selection accuracy, upfront lookup rate, parameter validity, and erroneous calls.74% similar
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