TLDR Tech / Data
Fixing What LLMs Get Wrong
Enterprise LLM systems can produce fluent but factually wrong answers against private structured knowledge, creating a “hallucination tax” on pricing, policy, org, and legal data. Fine-tuning, RAG, and static verification each help, but none learn from repeated failures. Reflexion closes the loop by storing natural-language reflections from verified errors in episodic memory and reinjecting them into future prompts.
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