Want to Become an LLM Engineer? Start With These Projects
Let’s be honest—breaking into AI, especially as an LLM engineer, can feel overwhelming at first. There’s a ton of theory, endless new tools, and a constant flood of updates that make you feel like you’re always one step behind.
But here’s the thing: you don’t need to know everything. You just need to start building.
Some of the best AI engineers I know didn’t get there by memorizing textbooks or taking a million courses. They built stuff—real projects that solved real problems. 
So if you’re serious about learning, here’s my advice: skip the passive learning loop and start working on projects that actually matter. Here are three solid ones to help you get started.
Project 1: A WhatsApp Bot That Summarizes YouTube Videos
📌 Why this is a great project:
Let’s be real—most of us have way too many YouTube videos saved in our “Watch Later” list that we’ll never actually watch. Imagine if you could just send a link to a chatbot, and it would return a quick, clean summary. Super useful, right?
How it works:
✅ User sends a YouTube link to the bot on WhatsApp.
✅ The bot checks if the link is valid (basic input validation is important!).
✅ It grabs the transcript of the video and feeds it to an LLM.
✅ The model summarizes the key points in a few sentences.
✅ The bot sends the summary back to the user.
Skills you’ll learn:
✔️ How to use YouTube’s transcript API
✔️ Prompt engineering for summarization tasks
✔️ Deploying a chatbot with Twilio’s WhatsApp API
👉 Take it further: Add support for multiple languages so people can get summaries in their native tongue.
Project 2: An AI-Powered Customer Support Assistant
📌 Why this is a great project:
Businesses spend millions on customer support. If you can build an AI-powered chatbot that automates responses for common inquiries, you’ve got a skill that companies actually need.
How it works:
✅ User asks a question (e.g., “What’s my flight status?”).
✅ The chatbot identifies the intent (flight status, baggage, booking, etc.).
✅ It chooses the right response template and fills in the details.
✅ The answer is sent back via WhatsApp.
Skills you’ll learn:
✔️ How to categorize user intents
✔️ Using LangChain’s Router to route different queries
✔️ Integrating with customer service APIs
👉 Take it further: Add a voice input feature so users can send voice notes instead of typing.
Project 3: A RAG-Powered Chatbot (The AI Companies Actually Want!)
📌 Why this is a great project:
Most AI chatbots today rely on static responses—meaning they can’t really adapt to new information. But with Retrieval-Augmented Generation (RAG), you can build a chatbot that pulls live data from documents to generate accurate, up-to-date answers.
How it works:
✅ You store a company’s documents (FAQs, policies, etc.) in embeddings.
✅ When a user asks a question, the chatbot searches the documents for relevant info.
✅ It retrieves the best answer and feeds it into an LLM.
✅ The model generates a response based on that info.
Skills you’ll learn:
✔️ How to use FAISS and Hugging Face to create embeddings
✔️ Implementing RAG with LangChain
✔️ Fine-tuning chatbots for business use cases
👉 Take it further: Connect it to real-time data sources like internal company databases.
Why These Projects Matter
Look, I get it. It’s easy to get stuck in tutorial hell, watching endless AI videos and reading papers but never actually building something useful.
But these projects? They force you to apply what you’ve learned in the real world. They’ll help you understand:
✅ How to work with real APIs
✅ How to integrate LLMs into actual applications
✅ How to build AI tools that people actually want to use
And most importantly? They’ll give you something to show when you apply for jobs or freelance gigs.
If you’ve made it this far, you’re probably serious about becoming an LLM engineer. And trust me—the best thing you can do is start building.
Pick one of these projects. Start small, make mistakes, iterate, and improve. That’s how real engineers learn.
So…which one are you going to build first? 🚀
