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Coles Python AI Programming, Second Edition: Kickstart developing AI-ready apps with RAG, DSPy, MCP, agents, evals, observability and open-source models at Bayshore Shopping Centre in Ottawa, ON
Python AI Programming, Second Edition: Kickstart developing AI-ready apps with RAG, DSPy, MCP, agents, evals, observability and open-source models in Ottawa, ON
Python AI Programming, Second Edition: Kickstart developing AI-ready apps with RAG, DSPy, MCP, agents, evals, observability and open-source models in Ottawa, ON
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The way we develop AI has completely changed. I mean, who builds models from scratch these days? Nobody.
Today's developers are creating apps based on existing models, and this second edition teaches you how to do that in Python. We start with one API call and end up with a full production service that's all set to go. As you go through the chapters, you build one application that grows with you, so you never throw anything out. With retrieval, you can supply real documents , get reliable, structured output with Pydantic schemas , allow DSPy to optimise your prompts against a measured metric , and give your assistant eyes, ears and hands through vision, speech and tool calling .
The book is deliberately practical. There's no backpropagation or transformer that's been hand-built. Instead, there are working files, realistic cost estimates, actual failure modes, and the judgment to know when fine-tuning is worth it and when it's not . The rest of the book looks at stuff like the Model Context Protocol, evaluation sets that spot regressions before customers do, safety and privacy checks, and observability with FastAPI and OpenTelemetry .
Key Learnings
Call hosted models reliably with retries, streaming, and controlled temperature settings.
Enforce structured output using Pydantic schemas with automatic validation and correction.
Build retrieval pipelines that ground every reply in your own documents.
Prepare messy PDFs and web content into passages worth embedding.
Optimise prompts programmatically with DSPy instead of hand-tuning them forever.
Judge honestly when fine-tuning repays its cost and when it never will.
Extract structured orders from photographs and transcribe spoken requests accurately.
Give models tools safely, with step limits and confirmation gates.
Expose capabilities through Model Context Protocol server's reusable across applications.
Measure quality with eval sets, then trace cost and latency in production.
Table of Contents
Working with Models
Prompts that Works
Reliable Outputs and Conversations
Programming Prompts with DSPy
Embeddings and Semantic Search
Retrieval-Augmented Generation
Getting Data Ready
Model Needs Customization
Working with Images and Sound
Tools, Agents and MCP
Examining AI with Evals and Safety Tests
Shipping with FastAPI and OpenTelemetry
The way we develop AI has completely changed. I mean, who builds models from scratch these days? Nobody.
Today's developers are creating apps based on existing models, and this second edition teaches you how to do that in Python. We start with one API call and end up with a full production service that's all set to go. As you go through the chapters, you build one application that grows with you, so you never throw anything out. With retrieval, you can supply real documents , get reliable, structured output with Pydantic schemas , allow DSPy to optimise your prompts against a measured metric , and give your assistant eyes, ears and hands through vision, speech and tool calling .
The book is deliberately practical. There's no backpropagation or transformer that's been hand-built. Instead, there are working files, realistic cost estimates, actual failure modes, and the judgment to know when fine-tuning is worth it and when it's not . The rest of the book looks at stuff like the Model Context Protocol, evaluation sets that spot regressions before customers do, safety and privacy checks, and observability with FastAPI and OpenTelemetry .
Key Learnings
Call hosted models reliably with retries, streaming, and controlled temperature settings.
Enforce structured output using Pydantic schemas with automatic validation and correction.
Build retrieval pipelines that ground every reply in your own documents.
Prepare messy PDFs and web content into passages worth embedding.
Optimise prompts programmatically with DSPy instead of hand-tuning them forever.
Judge honestly when fine-tuning repays its cost and when it never will.
Extract structured orders from photographs and transcribe spoken requests accurately.
Give models tools safely, with step limits and confirmation gates.
Expose capabilities through Model Context Protocol server's reusable across applications.
Measure quality with eval sets, then trace cost and latency in production.
Table of Contents
Working with Models
Prompts that Works
Reliable Outputs and Conversations
Programming Prompts with DSPy
Embeddings and Semantic Search
Retrieval-Augmented Generation
Getting Data Ready
Model Needs Customization
Working with Images and Sound
Tools, Agents and MCP
Examining AI with Evals and Safety Tests
Shipping with FastAPI and OpenTelemetry
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