The Large Language Model Notebooks Course is the unofficial half of an Apress book, and the book has more than the repository

The repository is the unofficial companion to a commercial book

The first thing the README says, in a two-column table with an Amazon short link in the empty left cell, is that this is the unofficial repository for the book Large Language Models: Apply and Implement Strategies for Large Language Models, published by Apress. The relationship runs the unusual direction: the book is based on the content of this repository, the notebooks here are being updated with new examples and chapters, and the official repository with the notebooks in their original book format lives at Apress/Large-Language-Models-Projects. Purchase links point at Amazon and at Springer, the latter carrying a DOI.

There is also an explicit coverage warning: the course on GitHub does not contain all the information that is in the book. So this is not a free transcription of a commercial text. It is the working material, and the book is a superset.

The licence on the repository is MIT, in a file named LICENSE.md. Nothing in the tree suggests otherwise, and the distinction that matters for a reader is that the notebooks here carry the author's continuing edits while the Apress repository carries the frozen versions that match the printed chapters.

The chapter folders run past the README's own topic list

The README describes three sections: techniques and libraries, then projects, then enterprise solutions. The topic list for the first section names Chatbots, Code Generation, the OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, model evaluation and knowledge distillation. The projects section is described as explaining design decisions, with LLMOps touched on as a secondary concern, and the enterprise section as an exploration of how models fit into large organisations.

The directory listing tells a slightly different story. The numbered chapters run 1-Introduction to LLMs with OpenAI, 2-Vector Databases with LLMs, 3-LangChain, 4-Evaluating LLMs, 5-Fine Tuning and 6-PRUNING. Pruning has its own chapter directory but does not appear anywhere in the README's list of covered topics, which is the clearest case of the repository being ahead of its own table of contents.

The other half of the tree is the numbered project work: P1-NL2SQL, P2-MHF and P3-CustomFinancialLLM for the projects section, E1-NL2SQL for big Databases and E2-Transforming Banks With Embeddings for the enterprise section, and a Datasets directory. The abbreviations are not expanded in the visible text, so P2-MHF in particular has to be worked out from the notebook. A clean_notebooks.py script sits at the root, an img directory holds images, and a single diagram image, LLM_course_diagram.jpg, is committed at the top level.

A .DS_Store and a checkpoint directory are committed at the root

Two entries in the root listing are local artefacts that no published repository needs. .DS_Store is the Finder metadata file macOS writes into any folder a user browses, and .ipynb_checkpoints is the directory Jupyter creates to hold autosaved copies of open notebooks. Both are present in the checkout, which means they are tracked rather than ignored.

For a repository whose deliverable is code that people copy into Colab, that has a practical effect. A notebook copied from a directory containing checkpoints can arrive with a duplicated autosaved version attached, and the checkpoint copies are the ones nobody re-ran. It also suggests the ignore rules were written once and not revisited, which is not a criticism so much as a signal about how the repository is assembled: notebooks are produced locally in a Jupyter environment and published outward, rather than edited through a review process.

The clean_notebooks.py script at the root is the tool that handles the publication side, and its existence supports that reading: the workflow is to strip and normalise a working notebook before it goes up, which is exactly what you need when the authoring environment leaves checkpoints behind.

Colab is the assumed runtime and memory is the stated constraint

The course has a section on how to use it, and the practical advice is worth more than the syllabus. Each lesson is made of notebooks plus articles: the notebook carries enough explanation to understand the code in it, and the article goes into more detail on the code and the topic. The recommendation is to keep the article open next to the notebook and follow along, because many articles offer small variations you can introduce, and the author recommends taking them.

The hosting story is equally specific. Most notebooks are on Colab and a few are on Kaggle, because Kaggle gives more memory in its free version than Colab does, while copying and sharing notebooks is simpler in Colab and not everyone has a Kaggle account. So the choice between the two hosts is a memory-versus-access trade, decided per notebook.

The limitation that recurs is spelled out: some notebooks need more memory than the free version of Colab provides, and since the work is with large language models this will keep happening. The two outs given are running the notebooks in your own environment, or paying for Colab Pro. That is the honest constraint of the whole course, and it is why several later chapters, the fine tuning and pruning ones in particular, are the ones a reader hits the wall on.

The first lesson tries to turn a prompt into a security control

Chapter 1 works with the OpenAI API through two small projects, and the second one is where the interesting material sits. The first is a restaurant chatbot that takes customer orders, built with GPT-3.5, with the aim of covering prompt engineering fundamentals, the OpenAI roles, temperature settings and how to avoid prompt injections. The same chatbot notebook exists in two front-end versions, one on Panel and one on Gradio, each with its own article.

The second project builds on the first to make an SQL statement generator, and the stated goal is to create a secure prompt that only accepts SQL creation commands and nothing else. That phrasing is the whole lesson and also its warning. A prompt that asks a model to refuse everything except one instruction class is a probability statement, not an access control, and the course presents it as a design goal to attempt rather than as a guarantee.

The natural language to SQL translator follows the same frame with more moving parts: the model has to be given the table structures, and the prompt was adjusted to keep it working and avoid malfunctions. A community contribution from a user named fmquaglia redid the schema description with DBML, and the author says in the text that it is by far a better approach than the original. That is the most valuable line in the chapter, and it is a reader improvement rather than a documented feature.

The home page is a Medium series, not documentation

The repository's homepage is not a documentation site. It is a Medium profile list for the course series, which tells you where the written content actually lives. Every notebook is paired with a Medium article that explains the code in detail, and the chapter titles in the README are links into the lesson folders rather than into separate documentation pages.

So the knowledge lives in two places that are versioned differently. The notebooks are in the repository and move with the main branch, whose last commit is dated 2026-09-29, with no tagged release and no version pinned anywhere in the tree. The articles are on a publishing platform, where URLs carry identifiers rather than revisions. A lesson link that breaks or an article that predates the notebook it explains are both ordinary outcomes.

The drift is visible in the model naming too. The first chapter builds against GPT-3.5 while the repository is being pushed in late 2026, and the chapter list still reads as a 2023 to 2024 syllabus, which matches the API surface described in it: roles, temperature and prompt engineering. That is a course frozen in the vocabulary of its moment rather than a maintained reference, and it is more usable as an introduction than as a current API guide.

Editorial conclusion

Work through this as a course rather than as a library: the value is the pairing of a notebook with a written explanation of why it is written that way, and the cost is that the model versions, hosting assumptions and chapter coverage all drift without a release to pin them. Take the techniques chapters as background, treat the prompt-security lesson as a lesson about prompts rather than a control you can deploy, and buy the book if you want the material that is deliberately not here.

Frequently asked questions

Official sources

1. Issues
2. License: MIT
3. peremartra/Large-Language-Model-Notebooks-Course on GitHub
4. Project website
5. README