ai-engineer-notebooks

Ai-engineer-notebooks provides a set of Google Colab notebooks that let you work with core AI‑engineer building blocks without committing to a particular deep‑learning framework. The notebooks are grouped around model API interaction, structured JSON output, tool calling, retrieval‑augmented generation (RAG), evaluation loops, and agent‑style looping. Each notebook opens in Colab, so you can start executing cells immediately after granting any required permissions.

The collection is intended for forward‑deployed engineers, AI engineers, and developers who are preparing for senior‑level technical interviews or transitioning into machine‑learning roles. If you already understand the theory behind prompts and want concrete, runnable examples that illustrate how to call an LLM endpoint, parse its structured response, and feed that result into a downstream tool, these notebooks match that need. Engineers who prefer a full‑stack framework such as LangChain may find the framework‑free approach less convenient, and beginners without any familiarity with API keys or JSON handling might need additional guidance before the notebooks become productive.

Using a notebook begins with a brief setup cell where you paste an API key for the chosen model provider (the notebooks do not lock you to a single vendor). Subsequent cells walk you through constructing a prompt, sending it to the model via a REST call, and receiving a JSON payload that follows a predefined schema. The output cell then demonstrates how to extract fields from that JSON and pass them to a simulated tool, such as a search function or a calculator, using a simple function call. For RAG scenarios, you supply a small text corpus or a URL, and the notebook shows how to embed the documents, retrieve the most relevant passages, and include them in the prompt. Evaluation cells compare the model's answer against a reference output, reporting basic metrics like exact match or token‑level similarity. The agent loop notebooks extend this pattern by repeating the call‑extract‑tool cycle until a termination condition is met, illustrating how an autonomous agent can iteratively refine its answer.

All notebooks are hosted on calm.rocks, a site that also offers career‑development programs such as interview‑prep cohorts and 1:1 advisory. The notebooks serve as a technical complement to those programs, giving you hands‑on practice that aligns with the structured, habit‑focused learning approach described on the site.