10 Lessons from Developing an AI Chatbot Using Retrieval-Augmented Generation
Fiddler users needed a way to easily find answers from the company's documentation, while the development team faced challenges around LLM context window limits, diverse natural language query patterns, and chatbot hallucinations.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits documentation query
Fiddler users submit queries to the chatbot to find answers from Fiddler's documentation.
Tools used
LangChainGPT-3.5Fiddler LLM ObservabilityRAG
Outcome
Fiddler deployed a RAG-based documentation chatbot using GPT-3.5 and LangChain, continuously monitored with Fiddler LLM Observability. Hallucinations were mitigated through iterative knowledge base enrichment, and switching to streaming responses significantly enhanced user trust and conversational experience.
What failed first
During development the chatbot hallucinated by misinterpreting the acronym 'LLM' as 'local linear model' instead of 'large language model', highlighting a gap in the knowledge base; initial static block response formatting also felt disjointed to users.