Unverified50% confidenceOpinionExact time
Most 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.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
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.73% similarVerifiedHallucination problems in LLM code generation manifest as fabricating non-existent APIs, incorrectly combining parameter orders of real APIs, or generating deprecated usage based on outdated documentation.71% similarUnverifiedData generated by directly prompting LLMs or by conditioning on usage-scenario constraints lacks diversity and tends to collapse into a few dominant patterns.68% similarUnverifiedLLMs have a strong tendency toward error tolerance and completion, and when faced with misaligned table data they typically do not report errors but instead produce seemingly reasonable inferences based on the erroneous data.68% similar
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