Prompt Engineering Notebooks: 22 Techniques, One Jupyter Collection

What This Repository Actually Is

This is not a tool or a framework. It is a collection of 22 Jupyter notebooks, each demonstrating a prompt engineering technique. The README calls it a comprehensive resource for learning, building, and sharing, and the topics range from basic prompt templates to advanced methods like chain-of-thought, self-consistency, and tree-of-thought prompting. The intended audience is someone who wants to learn by doing, likely a developer or data scientist already comfortable with Python and notebook environments. The repository's primary language is Jupyter Notebook, which tells you the format: runnable examples rather than abstract theory. The homepage links to diamant-ai.com, a commercial site, and the README heavily promotes a paid course called Prompt to Production. That context matters when you evaluate the material's neutrality.

The 22 Technique Curriculum

The repository's core value is its structured walkthrough of prompt engineering methods. The README explicitly lists chain-of-thought, self-consistency, and tree-of-thought as advanced techniques, and it promises coverage from fundamental concepts to advanced strategies. Based on the description, you can expect notebooks that introduce a technique, explain its rationale, and show a practical implementation. For a beginner, that progression is useful because it mirrors how the field evolved: first you learn to write a clear prompt, then you learn to structure reasoning, then you learn to sample multiple reasoning paths. The repository does not claim to cover every possible technique, and it does not. It is a curated set, not an encyclopedia. If you already know the basics, you might skip the early notebooks and jump to the advanced ones.

How the Notebooks Work in Practice

The README does not include a quick start section, installation instructions, or a list of required API keys. You must infer the workflow from the repository's nature. Each notebook likely contains Python code that calls an LLM provider, probably OpenAI or Anthropic, given the topics list mentions gpt, chatgpt, and claude. The techniques are demonstrated through code cells that you run sequentially. For example, a chain-of-thought notebook might show a prompt that asks the model to reason step by step, then compare that output with a direct answer. The repository also lists langchain as a topic, so some notebooks may use that framework to orchestrate prompts. Without explicit dependency files, you will need to install packages manually, and you will need your own API credentials. The README mentions an AI assistant that can be added to Claude Code via npm install, but that is for the paid course, not for this repository.

Getting Started: What You Need to Run It

The README does not provide a single setup command. Based on the repository layout, you would clone the repository, open the notebooks in Jupyter, and run them cell by cell. You will need a Python environment with jupyter installed, plus the OpenAI, Anthropic, or LangChain packages depending on the notebook. The repository's topics list confirms these dependencies. You will also need API keys for the respective LLM providers. There is no mention of a requirements.txt or environment.yml in the README, so you must inspect each notebook to see its imports. That is a real friction point. If you are used to projects that run with one pip install command, this repository will feel less polished. The README's focus on course promotion suggests the notebooks are a teaser for the full course, and the setup is left to the learner.

Licence and Maintenance: The NOASSERTION Problem

The repository's license is marked NOASSERTION, which means GitHub could not identify a standard open source license. That has practical consequences. You cannot assume you have the right to copy, modify, or redistribute the notebooks, and the README's heavy promotion of a commercial course makes it likely that the author retains all rights. For an engineer evaluating adoption, this is a red flag. The repository is not archived, and the last push was on September 4, 2026, which suggests recent activity. However, there are no recent releases listed, so you cannot rely on tagged stable versions. The README also includes a newsletter signup and YouTube channel, indicating that the author uses this repository as a marketing funnel. None of that invalidates the technical content, but it means you should treat the repository as a personal teaching resource, not a community-governed project.

Where It Falls Short: Thin Documentation and Vendor Lock-In

The most obvious limitation is the lack of setup documentation. The README is long but spends most of its space on course ads, sponsor logos, and social media links. It does not explain how to run the notebooks, what versions of libraries are required, or whether the examples work with both OpenAI and Anthropic models. Another limitation is the potential for API drift. LLM providers change their APIs frequently, and without release tags or a CI pipeline, the notebooks may break silently. The repository's topics include langchain, which itself changes often. If you rely on these notebooks for a production decision, you will need to validate each one against your chosen model. The README also claims the repository is one of the most extensive collections available, but that is a marketing claim, not a verifiable fact. You should judge the content on its own merits.

Alternatives: What Else to Consider

If you want a more formal, library-based approach to prompt engineering, consider LangChain. LangChain provides a structured framework for chaining prompts, managing memory, and integrating with multiple models. The difference in approach is significant: this repository teaches you individual techniques in isolation, while LangChain gives you a set of abstractions to build applications. Another alternative is the OpenAI Cookbook, which offers official, OpenAI-maintained examples that are likely to stay current with API changes. The Cookbook focuses on OpenAI models specifically, whereas this repository claims broader coverage including Claude. If you prefer video learning, the README points to the author's YouTube channel, but that is not an alternative tool, just another format. For a team needing maintainable prompt code, a framework like LangChain or a prompt management tool would be more appropriate than a collection of standalone notebooks.

Upgrade and Maintenance Cost

Because this is a notebook collection, upgrading is manual. There is no package version to bump. When a new technique is added or an existing notebook is fixed, you must pull the repository and re-run the affected notebooks. The README mentions that the course is live, and the repository may receive updates, but without release notes you will not know what changed. The maintenance cost is low if you use the notebooks for learning, but high if you depend on them for reproducible experiments. The lack of a license also complicates any internal redistribution. You can use the notebooks for personal study, but you cannot legally incorporate the code into a proprietary product without permission. That alone may disqualify the repository for many engineering teams. If you are an individual learner, the cost is just your time and API usage.

Editorial conclusion

Adopt this repository if you are a developer or student who learns best by running notebooks and wants a structured path from prompt basics to advanced reasoning techniques. Do not expect a maintained software library, an official license, or neutral documentation, the README is dense with course and newsletter promotion. Before relying on it, verify that the notebooks run with your current OpenAI, Anthropic, or LangChain versions, since the repository has no release tags and last activity is from September 2026. If you need production-grade prompt management or a formal framework, look elsewhere.

Official sources

1. Issues
2. NirDiamant/Prompt_Engineering on GitHub
3. Project website
4. README