Large Language Models for Mortals: A Practical Guide for Analysts with Python
by Andrew Wheeler
TABLE OF CONTENTS
Preface
Are LLMs worth all the hype?
Is this book more AI Slop?
Who this book is for
Why write this book?
What this book covers
What this book is not
My background
Materials for the book
Feedback on the book
Thank you
1 Basics of Large Language Models
1.1 What is a language model?
1.2 A simple language model in PyTorch
1.3 Defining the neural network
1.4 Training the model
1.5 Testing the model
1.6 Recapping what we just built
2 Running Local Models from Hugging Face
2.1 Installing required libraries
2.2 Downloading and using Hugging Face models
2.3 Generating embeddings with sentence transformers
2.4 Named entity recognition with GLiNER
2.5 Text Generation
2.6 Practical limitations of local models
3 Calling External APIs
3.1 GUI applications vs API access
3.2 Major API providers
3.3 Calling the OpenAI API
3.4 Controlling the Output via Temperature
3.5 Reasoning
3.6 Multi-turn conversations
3.7 Understanding the internals of responses
3.8 Embeddings
3.9 Inputting different file types
3.10 Different providers, same API
3.11 Calling the Anthropic API
3.12 Using extended thinking with Claude
3.13 Inputting Documents and Citations
3.14 Calling the Google Gemini API
3.15 Long Context with Gemini
3.16 Grounding in Google Maps
3.17 Audio Diarization
3.18 Video Understanding
3.19 Calling the AWS Bedrock API
3.20 Calculating costs
4 Structured Output Generation
4.1 Prompt Engineering
4.2 OpenAI with JSON parsing
4.3 Assistant Messages and Stop Sequences
4.4 Ensuring Schema Matching Using Pydantic
4.5 Batch Processing For Structured Data Extraction using OpenAI
4.6 Anthropic Batch API
4.7 Google Gemini Batch
4.8 AWS Bedrock Batch Inference
4.9 Testing
4.10 Confidence in Classification using LogProbs
4.11 Alternative inputs and outputs using XML and YAML
4.12 Structured Workflows with Structured Outputs
5 Retrieval-Augmented Generation (RAG)
5.1 Understanding embeddings
5.2 Generating Embeddings using OpenAI
5.3 Example Calculating Cosine similarity and L2 distance
5.4 Building a simple RAG system
5.5 Re-ranking for improved results
5.6 Semantic vs Keyword Search
5.7 In-memory vector stores
5.8 Persistent vector databases
5.9 Chunking text from PDFs
5.10 Semantic Chunking
5.11 OpenAI Vector Store
5.12 AWS S3 Vectors
5.13 Gemini and BigQuery SQL with Vectors
5.14 Evaluating retrieval quality
5.15 Do you need RAG at all?
6 Tool Calling, Model Context Protocol (MCP), and Agents
6.1 Understanding tool calling
6.2 Tool calling with OpenAI
6.3 Multiple tools and complex workflows
6.4 Tool calling with Gemini
6.5 Returning images from tools
6.6 Using the Google Maps tool
6.7 Tool calling with Anthropic
6.8 Error handling and model retry
6.9 Tool Calling with AWS Bedrock
6.10 Introduction to Model Context Protocol (MCP)
6.11 Connecting Claude Desktop to MCP servers
6.12 Examples of Using the Crime Analysis Server in Claude Desktop
6.13 What are Agents anyway?
6.14 Using Multiple Tools with the OpenAI Agents SDK
6.15 Composing and Sequencing Agents with the Google Agents SDK
6.16 MCP and file searching using the Claude Agents SDK
6.17 LLM as a Judge
7 Coding Tools and AI-Assisted Development
7.1 Keeping it real with vibe coding
7.2 VS Code and GitHub Install
7.3 GitHub Copilot
7.4 Claude Code Setup
7.5 Configuring API access
7.6 Using Claude Code to Edit Files
7.7 Project context with CLAUDE.md
7.8 Using an MCP Server
7.9 Custom Commands and Skills
7.10 Session Management
7.11 Hooks for Testing
7.12 Claude Headless Mode
7.13 Google Antigravity
7.14 Best practices for AI-assisted coding
8 Where to next?
8.1 Staying current
8.2 What to learn next?
8.3 Forecasting the near future of foundation models
8.4 Final thoughts
Andrew Wheeler 