In the world of communication, sometimes you need to send a formal, one-way memo, and other times you need a dynamic, back-and-forth conversation. LLM providers offer models optimized for both scenarios, and LangChain gives us a clean, standardized way to interact with each. After mastering Prompt Templates, the next logical step in our chain is … Read more
If the LLM is the engine of your AI application, then the prompt is the fuel. The quality, structure, and clarity of your prompt directly determine the performance of your entire system. While you could just use Python’s f-strings to create prompts, this quickly becomes messy, hard to reuse, and doesn’t integrate with the powerful … Read more
In our last post, we built our first application using a magical | symbol to connect our components. This pipe operator wasn’t just a convenient shortcut; it’s the core of the modern LangChain framework, known as the LangChain Expression Language (LCEL). LCEL is a declarative way to compose chains. Instead of writing complex, step-by-step procedural … Read more
The theory is over. It’s time to write code. For twenty-three posts, we’ve journeyed through the concepts that power Agentic AI, from the ReAct loop to memory and planning. We’ve built a professional workshop with Python, VS Code, and Git. Now, we finally get to turn on the machines. We’ll begin our practical journey with … Read more
In our journey through the world of Agentic AI, it’s easy to become captivated by the ultimate technical challenge: creating a fully autonomous agent that can take a goal and execute it from start to finish with no human intervention. It’s a compelling vision of a “fire-and-forget” intelligence. But in the real world, is that … Read more
A Large Language Model, for all its power, is a closed book. Its knowledge is vast but frozen in time, limited to the data it was trained on. It doesn’t know about yesterday’s news, your company’s latest quarterly report, or the specifics of your private project files. So, how do we build agents that can … Read more
Imagine you ask a brilliant but very eccentric assistant to get a new contact’s information. You need their name, phone number, and email to add to your database. They come back and say: “Oh, for sure! I spoke with Dr. Aris Thorne. A wonderful person! You can reach him at 555-0199. His email, he mentioned, … Read more
If you ask a person to “plan a vacation,” they don’t just stare blankly. Their mind instantly begins a process of task decomposition. A large, vague goal (“plan a vacation”) is broken down into a sequence of smaller, concrete, and achievable steps: This ability to create a structured plan is a hallmark of intelligence. For … Read more
Imagine handing a power drill to someone who has never seen one before. You don’t give them a manual, the buttons aren’t labeled, and you just say, “Here, use this thing.” The result would be confusion at best, and a hole in the wall at worst. This is what it’s like to give a poorly … Read more
Think about your own memory. It isn’t a single, monolithic thing. You have short-term memory that holds what someone just said to you, and you have long-term memory that stores foundational experiences from your childhood. To be truly intelligent and useful, an AI agent needs both. We’ve previously discussed context windows—the finite amount of information … Read more