Unverified50% confidenceSolutionExact time
LLM application failures can be divided into two categories: knowledge access problems (the model cannot retrieve the correct information) and behavioral problems (the model has the information but fails to process it as required).
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
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Related Claims
UnverifiedThe root causes of problems in LLM applications are often not code logic errors, but soft factors such as prompt wording, context management, and retrieval strategies, which are difficult to automatically fix with deterministic rules.75% similarUnverifiedMost of what goes wrong in LLM features is not in the model itself but in the surrounding code, such as building the messages list, selecting the model ID, parsing JSON, handling an empty choices list, retrying on 429s, and counting tokens.75% similarUnverifiedThe root cause of poor knowledge base performance typically lies in unreasonable document chunking strategies, inappropriate vector model selection, or insufficient retrieval recall quantity, rather than issues with the model itself.69% similarUnverifiedAn intent error means the LLM fundamentally misunderstood the user's true need, thereby selecting the wrong tool or calling a tool when none should have been called.69% similarUnverifiedError messages should be written for the model to read and must be "actionable" rather than "technically correct" — throwing raw parser errors at the model is of no value.69% similar
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