Building a Chatbot With Large Language Models

Start by defining scope and personality — what topics to handle, what to refuse, what tone to use. Knowledge grounding through RAG (retrieval-augmented generation) connects the LLM to accurate, current domain information, dramatically reducing hallucination. Conversation management handles stateful multi-turn dialogues with context tracking and session state management.

Testing and iteration are ongoing — build evaluation datasets covering common queries, edge cases, and adversarial inputs. Monitor production conversations for failure patterns and use each identified failure as a test case that drives improvement in the system prompt, knowledge base, or guardrails.