AI Hallucination
"The phenomenon where a Large Language Model generates information that is factually incorrect or logically inconsistent while maintaining a confident tone. Solved via Surgical Architecture."
Practical Usage
Hallucination is a natural byproduct of how LLMs work—they predict the next most likely token based on probability, not verification. To eliminate hallucinations in production, you must build guardrails around the model: feeding it validated source documents (RAG), checking outputs with code compilers, or using multiple agents to review each other's work.
How it Works
Implement Retrieval-Augmented Generation (RAG). Add a system constraint: "If the provided context does not contain the answer, reply with 'I do not know'. Do not make up information."
Author Note
"Never trust an LLM to remember facts. Always retrieve the facts from a database or search engine first, and then instruct the LLM to write the response *only* using that retrieved context."