Unverified50% confidenceFactExact time
The LLM itself does not understand the internal structure of the enterprise data lake, so when the prompt lacks table names, field names or join relationships, the model can only guess when generating SQL, and the error rate stays high.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedHaving LLMs directly generate SQL is high-risk, because the model does not understand table structures, field meanings, or business definitions, and easily generates queries that are syntactically correct but produce wrong results.80% similarUnverifiedAn LLM has no fact database it can consult; its weights are tuned to assign high probability to a plausible next token, generating one token at a time.74% similarUnverifiedLLMs do not maintain a factual database and lack the metacognitive ability to know 'whether an operation was actually performed'; when the context strongly implies a certain output pattern, they generate text conforming to that pattern with high confidence, even if the content is entirely fabricated.74% similarVerifiedLLM architectures cannot strictly distinguish between 'instructions' and 'data' at the semantic level, which is the root cause of why prompt injection is difficult to fundamentally eliminate.72% similarUnverifiedLLM 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).69% similar
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