Description
Large Language Models for Mortals is Crime De-Coders second book — an introduction to using python with all the major LLM foundation model providers (OpenAI, Anthropic, Google, and AWS Bedrock).
This practical introduction covers the basics of what data scientists deploying applications, social scientists analyzing large amounts of data, and developers looking to get their feet wet with LLM coding applications need to keep up in a vastly advancing area.
Unlike its competitors, the book lays out both the entry level aspects of using LLM APIs (temperature, stop sequences, log probs, measuring costs). But also goes into more advanced concepts that are regular components of production systems, such as structured outputs and batch processing, RAG systems (including examples of in-memory, on disk, and different offerings from OpenAI, AWS, and Google), and MCP/tool-calling/Agents. It also includes practical examples of local models for certain tasks (OCR and NER) that can be run on a CPU.
Each of the chapters has extensive examples from the major foundation providers — OpenAI, Anthropic, Google, and AWS Bedrock. Additionally there is a chapter on LLM coding tools (GitHub Copilot, Claude Code, and Google’s Antigravity editor).
If you want to get started building real LLM applications using foundation models, this is the book you need. You can preview the first 60 plus pages at this blog post.
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