LLM Projects for Absolute Beginners

Reading about transformer architectures and attention mechanisms is helpful, but real learning happens when you connect a Large Language Model (LLM) to an external database or let it run code. To truly understand how LLMs work in practice, you need to build projects. In this article, I’ll walk you through four LLM projects for absolute beginners that will help you master the basics.

LLM Projects for Absolute Beginners

Here’s a look at four essential LLM projects. These will introduce you to the main tools and ideas in modern AI engineering, along with the guided examples to get started.

Build a Task Planning AI Agent

Moving from a basic chatbot to an AI agent changes who is in control. In a chatbot, the user leads the conversation. In an agent, the LLM gets a main goal and figures out the steps to reach it.

An AI agent is an LLM inside a control loop, often using patterns like ReAct, which stands for Reasoning and Acting. You give the LLM a system prompt that explains its goal and a list of tools, which are just Python functions you create.

When you give the agent a task, it does more than just write text. It creates a plan, picks the right tool from its list, gives the tool’s parameters, and waits for your Python script to run the function and send back the results. It repeats this process until it reaches the main goal.

Here’s an example to build a task planning AI Agent.

If you’re serious about mastering real-world LLM concepts like this, I’ve broken them down step-by-step in my book: Hands-On GenAI, LLMs & AI Agents.

Building a Document Q&A System

LLMs only know what they learned during training. If you ask about your company’s onboarding document or a PDF that just came out, the LLM might make up an answer. Retrieval-Augmented Generation (RAG) helps solve this problem.

RAG is a way to connect an LLM to outside data. To build it, first pull the text from your document, like a PDF, and break it into smaller pieces. Next, use an embedding model to turn these pieces into vectors; arrays of numbers that capture the meaning of the text.

When someone asks a question, you turn it into a vector using the same model. Then, search your vector database to find the most relevant text pieces and add those directly to the LLM’s prompt.

Here’s an example to build a document Q&A system.

Turn Any CSV into an AI Chatbot

Business teams often need to look up information in tables, like sales sheets, user logs, or financial forecasts, but many people don’t know SQL or Python. Creating a system that turns plain language into data analysis is very valuable.

Many people think you should give the whole CSV file to the LLM, but for big datasets, this is costly, slow, and often leads to mistakes.

A better way is to use code generation. Give the LLM just the schema, the column names and data types ,of your CSV, not the full data. When someone asks a question, the LLM writes a Python script with Pandas to find the answer. Your system runs this code safely, collects the result, and sends it back.

Here’s an example to build an AI chatbot using a CSV file.

Build an LLM-Enabled MCP Server

As you create more AI apps, you’ll notice that making custom API connections to different data sources, like Slack, Google Drive, or local databases, is repetitive and can break easily. The Model Context Protocol (MCP) was designed to fix this.

MCP is an open standard that makes it easier for AI models to talk to outside data sources and tools. It’s like USB-C, but for AI. Instead of writing new integration code for each LLM, you set up an MCP Server.

This server makes your local files, databases, or company APIs available in a standard way. An MCP Client, like an IDE, desktop AI assistant, or web app, can connect to your server and quickly see what data and tools it can use.

Here’s an example to build an LLM-Enabled MCP Server.

Summary

Here are the key LLM projects for beginners that will help you learn the basics of modern AI engineering:

If you found this article helpful, you can follow me on Instagram for daily AI tips and practical resources. You may also be interested in my latest book, Hands-On GenAI, LLMs & AI Agents, a step-by-step guide to prepare you for careers in today’s AI industry.