OpenAI’s API guide covers prompt engineering strategies and tactics for working with large language models.
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8 alternativesThis blog covers machine learning applications, with a focus on PyTorch examples and deep learning code.
About Blog
This is a technical blog post that explains how an AI agent guided by a large language model (LLM) works, and shows how to build a simple one from scratch. The author built a small library for this purpose, inspired by the langchain framework, with the stated goal of understanding agent behavior in very few lines of code. The post is part of a blog that covers machine learning, PyTorch examples, and deep learning code.
The post describes the agent's loop of Thought, Action, and Observation. The LLM generates the thoughts and actions, while tools produce the observations, such as the output of Python code or the results of a web search. It also shows the prompt template, the tool interface, and the main loop code. The excerpt includes example tools for Python execution, Google search, and Hacker News search. It covers a simple agent only and does not describe the full feature set of larger frameworks. The text is cut off before the section on reducing hallucination.
Key features
- Explains the Thought, Action, and Observation loop of an LLM agent
- Builds a simple agent from scratch as a small Python library
- Defines tools as Python classes with a name, description, and use method
- Includes an example tool that executes Python code in a REPL
- Includes example tools for Google search and Hacker News search
- Shows a prompt template that adds the date, tools, question, and prior responses
Best for: Developers and students who want to understand the internal mechanics of LLM agents by reading and adapting a small implementation.
Good for
- Shows the core parts of an agent in a small amount of code
- Includes the prompt template and main loop code, not just a conceptual overview
- Explains the reason for each prompt input, such as adding the current date
Watch out for
- Covers a simple agent, not the full feature set of larger frameworks such as langchain
- The excerpt ends before the section on reducing hallucination, so that part is not covered here
Blog at a glance
- Category
- AI & Machine Learning › AI Learning & Guides
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
Blog alternatives
Tools and sites you could use instead, ranked by how closely they match.
The Prompt Engineering Guide teaches prompt engineering and provides learning materials about language models.
AnimatedLLM uses animations to explain the mechanisms behind large language models.
Play Tensor Trust to explore prompt injection attacks and defenses through interactive challenges.
Test AI hacking skills and explore language model limitations through the Gandalf interactive challenge.
The UFLDL tutorial teaches core ideas in unsupervised feature learning and deep learning.
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Blog FAQ
Which tools does the agent in this post use?
The post describes tools for executing Python code in a REPL, using Google search, and searching Hacker News.
How does the agent know when to stop?
The loop ends when the LLM selects the Final Answer step. The main loop also has a maximum number of loops set by max_loops.
Why is the current date included in the prompt?
The author added it because the LLM would sometimes argue that a date was in the future, even after it received new information.
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