AI Hallucination
AI hallucination occurs when a large language model produces output that is fluent and confident-sounding but factually wrong — inventing features, citing nonexistent documentation, or providing incorrect instructions. In customer support, hallucinations are particularly dangerous because customers trust the AI to provide accurate product information.
Why hallucinations happen
LLMs generate text by predicting the most likely next token based on patterns in their training data. They don't have a concept of "truth" — they optimize for plausibility. When asked about specific product details they weren't trained on, they may generate realistic-sounding but incorrect answers rather than admitting uncertainty.
How RAG reduces hallucinations
RAG mitigates hallucinations by grounding the model's responses in your actual documentation. Instead of relying on the model's general knowledge, RAG retrieves specific passages from your docs and instructs the model to answer based only on that context. This dramatically reduces the chance of fabricated responses.
EchoSDK's approach
EchoSDK's RAG pipeline retrieves relevant documentation before generating any response. When the AI's confidence is low — meaning no sufficiently relevant documentation was found — EchoSDK automatically escalates the query to a human agent via the ticket system rather than guessing.
Related terms
Retrieval-Augmented Generation (RAG)
An AI technique that combines a language model with a retrieval system to generate answers grounded in specific documents or data sources.
Large Language Model (LLM)
An AI model trained on vast amounts of text data that can understand and generate human language, powering chatbots, summarization, and question-answering systems.
AI Confidence Score
A numerical measure of how certain an AI model is about its response, used to decide whether to serve the answer or escalate to a human agent.