A Google Colab notebook that only makes one API call to an LLM isn’t enough for a portfolio project anymore. That’s just a tutorial. If you want your portfolio to show you’re an AI Engineer, not just someone who uses APIs, you need to build full end-to-end systems. In this article, I’ll share three end-to-end LLM project ideas you can add to your portfolio.
End-to-End LLM Project Ideas for Portfolio
Here are three hands-on LLM projects that show you know both generative AI and software engineering.
Text-to-SQL App
Business users often need data, but most don’t know SQL. A Text-to-SQL app lets users ask questions in plain language, like “What were our top-selling products in Q3?” The app then turns that question into a correct SQL query and runs it on a live database.
This project is more than just asking an LLM to write code. You need to give the LLM the database schema, manage context windows, make sure the generated SQL can’t delete tables (using read-only permissions helps), and format the results so they’re easy to read.
To build a Text-to-SQL App, you can use LangChain or LlamaIndex to help with prompt creation. Set up a local PostgreSQL or SQLite database using sample e-commerce or HR data. For the frontend, try Streamlit or use a simple FastAPI backend with a React UI. Here’s a guided example to help you start this project.
Build an AI Data Analytics Web App
This project builds on the Text-to-SQL idea by adding agent-like workflows. Instead of only querying a database, users upload a raw CSV file. The AI acts like a junior data analyst: it cleans the data, writes Python code with Pandas to analyze it, and creates data visualizations based on prompts like, “Show me a trendline of monthly churn rates.”
To make this work, the LLM must write code, run it safely in a sandbox, check the results, and fix any errors if the code doesn’t work.
To build an AI Analytics Web App, Streamlit works well for the frontend. You can use frameworks like PandasAI or create a custom LangChain Agent with a Python REPL. Here’s a guided example to help you get started.
AI Code Review Bot for GitHub
Fast development is important, but code reviews can slow things down. An AI Code Review Bot acts as an automated helper that listens to GitHub webhooks. When a developer opens a Pull Request (PR), the bot gets the code changes, checks them against best practices, and posts comments right on the PR.
This project makes you consider system architecture and CI/CD pipelines. The LLM needs a clear system prompt so it doesn’t get too picky. It should focus on real bugs, security issues, or major anti-patterns, not small things like tabs versus spaces.
To build an AI Code Review Bot, set up a GitHub App or use GitHub Actions. Create a FastAPI service to receive webhooks, parse the .diff files, and send the code chunks to the LLM API. Then, use the GitHub REST API to post comments on the right lines of code. Here’s a guided example to help you get started.
Closing Thoughts
Here are the end-to-end LLM project ideas you can add to your portfolio:
When you build these projects, you’ll see that using the LLM is often the simplest part. In all three, calling the AI model only takes a few lines of code. The real challenge is in how you prepare the data for the model, process the results, and deal with mistakes when the model gets things wrong.
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.
