Building AI Chatbots with LLM APIs

The Problem

Building a reliable, production-grade AI chatbot is significantly more complex than calling an LLM API and displaying the response. Conversation context management, safety guardrails, cost control, hallucination handling, and graceful degradation on out-of-scope questions all require careful engineering that is not obvious from the API documentation alone.

How AI Helps

1. 01.Generates system prompt templates that define the chatbot's persona, domain scope, and fallback behaviour, giving developers a production-ready starting point rather than a blank prompt.
2. 02.Writes conversation state management logic for multi-turn conversations: tracking context, summarising old turns, and maintaining user preferences across sessions.
3. 03.Designs intent routing: a lightweight classifier that routes simple queries to a fast, cheap model and complex queries to a more capable one, optimising cost and latency simultaneously.
4. 04.Generates test cases for safety and quality evaluation: adversarial inputs, out-of-scope questions, and jailbreak attempts that should be handled gracefully.
5. 05.Writes the conversation evaluation pipeline — using an LLM judge to score response quality, helpfulness, and safety across a test set — enabling systematic quality monitoring.

Recommended Tools

Recommended Models

Example Prompts