Part II — Engineering with ChatML
ChatML, LLMs, Prompt Engineering, LangChain, LlamaIndex
“A language becomes powerful only when it meets engineering discipline.”
If Part I explored what ChatML is, Part II reveals how to build with it. This section translates structure and semantics into concrete engineering patterns — connecting syntax, templates, and system design into a working conversational framework.
Purpose of This Part
The goal of this part is to teach you how to operationalize ChatML — to turn markup concepts into running systems that communicate, remember, and reason. Through code, architecture, and modular design, you will learn how ChatML acts as the backbone for deterministic, reproducible AI conversations.
This is where prompt design meets software architecture. Each chapter walks you through a layer of the ChatML ecosystem — from message pipelines and template rendering to tool orchestration, memory management, and validation.
What You’ll Learn
By the end of this part, you will be able to:
- Build complete ChatML pipelines that manage roles, structure, and message flow.
- Design dynamic prompt templates using Jinja2 for flexible context rendering.
- Integrate tool invocation and function binding to enable LLM-driven actions.
- Implement persistent memory for contextual continuity across sessions.
- Establish observability and testing pipelines to ensure safety and reproducibility.
Each topic moves from theory → implementation → validation, showing how ChatML integrates naturally into modern AI stacks like FastAPI, LangChain, and Ollama.
Chapters in This Part
| Chapter | Title | Focus |
|---|---|---|
| 6 | Building a ChatML Pipeline | Designing the message flow, role logic, and context assembly. |
| 7 | Rendering with Templates | Using Jinja2 for dynamic and modular ChatML message generation. |
| 8 | Tool Invocation and Function Binding | Connecting ChatML with executable backend logic. |
| 9 | Memory Persistence Layer | Building context retention and long-term conversational memory. |
| 10 | Testing and Observability | Ensuring reliability through validation, monitoring, and logging. |
The Bridge from Theory to Practice
In Part I, ChatML was introduced as a language of structure — defining how humans and machines communicate.
In Part II, it becomes an engineering framework — defining how systems operate and interact.
You’ll move from writing messages to building the engines that process them. By combining clarity of syntax with discipline of design, this part lays the foundation for the fully functional Support Bot Project introduced in Part III.
💡 Part II is where structured prompting becomes structured engineering — turning theory into architecture and architecture into capability.