Part III — The Support Bot Project
ChatML, LLMs, Prompt Engineering, LangChain, LlamaIndex, Structured Prompting, AI Development, Conversational AI, OpenAI, GPT, Claude, FastAPI, Ollama, Tool Integration, Memory Management
“Theory becomes tangible when structure meets execution.”
This part transforms everything discussed so far — roles, templates, validation, and memory — into a living, deployable ChatML-based system. Here, you will build Support Bot v3.4, a fully functional conversational AI that embodies the principles of structured prompting, tool orchestration, and stateful reasoning.
Purpose of This Part
While Part I and Part II explored ChatML’s grammar and engineering discipline, this part shows how those ideas integrate into an operational product.
By the end, you will have a working FastAPI + Ollama (Qwen 2.5: 1.5 B) chatbot capable of handling customer-support workflows such as:
- Tracking and canceling orders
- Updating delivery addresses
- Checking refund eligibility
- Registering complaints
- Maintaining persistent, contextual memory
Each chapter dissects one layer of the architecture — from API endpoints and tool execution to logging, observability, and deployment.
What You’ll Learn
- How to convert structured ChatML messages into executable workflows.
- How intent detection, template rendering, and memory persistence interact in real-time.
- How to integrate FastAPI, Ollama, and local LLMs for reproducible, auditable AI.
- How logging and validation make conversational systems explainable and secure.
This part is deliberately hands-on: every concept culminates in runnable code, tested scripts, and transparent logs. You will see how ChatML acts not just as a markup, but as the nervous system of a production-ready conversational architecture.
The Journey Ahead
The chapter that follow demonstrate how ChatML as language becomes ChatML as infrastructure. You will watch a conversational framework evolve from theoretical markup into a working support assistant — transparent, modular, and human-aligned.
💡 Part III is where ChatML stops being documentation and becomes software — a bridge between structured conversation and operational intelligence.