I have published a new book, Large Language Models for Mortals: A Practical Guide for Analysts with Python. The book is available to purchase in my store, either as a paperback (for $59.99) or an epub (for $49.99).
The book is a tutorial on using python with all the major LLM foundation model providers (OpenAI, Anthropic, Google, and AWS Bedrock). The book goes through the basics of API calls, structured outputs, RAG applications, and tool-calling/MCP/agents. The book also has a chapter on LLM coding tools, with example walk throughs for GitHub Copilot, Claude Code (including how to set it up via AWS Bedrock), and Google’s Antigravity editor. (It also has a few examples of local models, which you can see Chapter 2 I discuss them before going onto the APIs in Chapter 3).
You can review the first 60 some pages (PDF link here if on Iphone).
While many of the examples in the book are criminology focused, such as extracting out crime elements from incident narratives, or summarizing time series charts, the lessons are more general and are relevant to anyone looking to learn the LLM APIs. I say “analyst” in the title, but this is really relevant to:
- traditional data scientists looking to expand into LLM applications
- PhD students (in all fields) who would like to use LLM applications in their work
- analysts looking to process large amounts of unstructured textual data
Basically anyone who wants to build or create LLM applications, this is the book to help you get started.
I wrote this book partially out of fear – the rapid pace of LLM development has really upended my work as a data scientist. It is really becoming the most important set of skills (moreso than traditional predictive machine learning) in just the past year or two. This book is the one I wish I had several years ago, and will give analysts a firm grounding in using LLMs in realistic applications.
Again, the book is available in:
For purchase worldwide. Here are all the sections in the book – whether you are an AWS or Google shop, or want to learn the different database alternatives for RAG, or want more self contained examples of agents with python code examples for OpenAI, Anthropic, or Google, this should be a resource you highly consider purchasing.
To come are several more blog posts in the near future, how I set up Claude Code to help me write (and not sound like a robot). How to use conformal inference and logprobs to set false positive rates for classification with LLM models, and some pain points with compiling a Quarto book with stochastic outputs (and points of varying reliability for each of the models).
But for now, just go and purchase the book!
Below is the table of contents to review – it is over 350 pages for the print version (in letter paper), over 250 python code snippets and over 80 screenshots.
Large Language Models for Mortals: A Practical Guide for Analysts with Pythonby Andrew WheelerTABLE OF CONTENTSPreface 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 you1 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 built2 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 models3 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 costs4 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 Outputs5 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 Judge7 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 coding8 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 