Through 'AI Engineer Notebooks,' a collection of open-source notebooks for AI developers and Forward Deployed Engineers (FDEs), we explore how to learn core AI technologies directly without complex framework dependencies.
Imagine you have signed up for a cooking class because you want to learn how to cook. But what if the instructor doesn’t teach you the principles of cooking, but only how to add a specific brand’s ‘all-purpose sauce’? If that sauce isn’t available or the recipe changes, you’ll be left unable to do anything.
In the rapidly growing field of AI, many developers are grappling with a similar dilemma. As countless complex frameworks (collections of tools to help with software development) and libraries are being churned out, opportunities to grasp the fundamental principles of how AI actually works are diminishing. A very welcome resource has been released for those facing this concern: ‘AI Engineer Notebooks’ [Source: GitHub - calmrocks/ai-engineer-notebooks]
Why Is This Important?
For those aspiring to be AI developers or Forward Deployed Engineers (FDEs), this material is like a textbook for learning the ‘basics of cooking.’ Many people create AI apps by relying on large-scale frameworks like LangChain. While convenient, they have the downside of making it difficult to understand what is happening under the hood when problems arise.
‘AI Engineer Notebooks’ allows you to call a model’s API (Application Programming Interface) directly and implement agents from scratch, without the help of such frameworks. This goes beyond simply writing code; it cultivates the ability to understand the core of AI systems [Source: GitHub - calmrocks/ai-engineer-notebooks]. The reason over 150,000 visitors seek out this resource every month is likely because they desire this ‘essential competence’ [Source: Trendshift].
Simply Put: ‘Framework-free’
Being ‘framework-free’ here is similar to turning off a camera’s auto mode and shooting in ‘manual mode (M mode).’ Auto mode creates beautiful photos just by pressing the shutter, but it often fails in low light or special situations.
In manual mode, you have to directly adjust the aperture, shutter speed, and ISO values. It’s a bit harder to learn, but once you master it, you become an expert who can take the photo you want in any environment. These notebooks allow you to handle the manual mode of the AI camera yourself.
Furthermore, this material emphasizes the important concept of ‘Evals-as-the-spine’ [Source: GitHub - calmrocks/ai-engineer-notebooks]. Just as you set up a frame before building a house, it suggests developing a habit of evaluating whether the system ‘works well’ with numbers first before implementing complex AI features in earnest [Source: 01-measuring-outputs.ipynb - Colab].
Current Status: What Can You Learn?
Currently, this collection of open-source notebooks is provided for free in a Google Colab environment, and you can implement the following core technologies from scratch [Source: GitHub - calmrocks/ai-engineer-notebooks, Source: Hacker News]:
- Model API Utilization: How to converse and communicate directly with AI models
- Structured Output: How to accurately receive only the data in the format you want from an AI
- Tool Calling: How AI uses external tools like calculators or search engines directly
- RAG (Retrieval-Augmented Generation): How AI reads vast external documents and provides answers
- Agent Implementation: How to set goals yourself and perform complex tasks while running loops (repeated execution of work)
- Security and Evaluation: How to block prompt injection attacks and objectively verify system performance
What Happens Next?
AI technology is changing day by day. However, engineers who deeply understand these principles will have a solid foundation that allows them to quickly adapt to any new framework that emerges.
Access Google Colab right now, build a basic system, and measure with numbers just how intelligently the AI you created actually answers. Are you ready to take the leap from a simple ‘prompt tinkerer’ to a ‘true problem-solving AI engineer’? [Source: 01-measuring-outputs.ipynb - Colab]
MindTickleBytes AI Reporter’s View
Trends in technology come and go like waves, but an understanding of principles remains solid like a rock. Before massive frameworks block your view, I strongly recommend that you secure experience built up from the ground up. This process of touching the essence of AI will make you a more profound engineer.
References
- It forces the use of specific development tools
- It implements core technologies directly without complex abstraction
- It uses only paid, not free, tools
- Tuning the model before measuring performance
- Building complex systems without question
- Evaluating the performance of a system with numbers before creating something
- RAG (Retrieval-Augmented Generation)
- Traditional web design techniques
- AI agent loops and tool calling