New

4 Llm-based Data Analysis Projects

Basic chatbots are no longer enough to impress hiring managers. Companies today are looking for AI Engineers who can integrate Large Language Models (LLMs) into real data workflows to solve business problems. They want to see skills like orchestration, error handling, and building systems that can process, query, and analyze complex data. In this article, I’ll walk you through four LLM-based Data Analysis projects you should build to get started in GenAI or AI Engineering.

LLM-Based Data Analysis Projects

Here are four practical, industry-focused LLM-based Data Analysis projects that can make a real difference on your resume.

1. Agentic AI Pipeline to Automate EDA

Exploratory Data Analysis (EDA) is always the first step in any data project, but it can get repetitive. Each time you work with a new dataset, you end up writing the same Pandas code to check for missing values, plot distributions, and find outliers.

Rather than relying on a single LLM call, try building a multi-agent system where different AI agents handle specific parts of the EDA process. For example, one agent can write Python code to profile the data, another can run the code in a safe environment, and a third can interpret the results and write a markdown report.

In practice, LLMs often write code that fails because they guess the wrong column names. The main engineering challenge is to create a feedback loop. If the execution agent runs into a KeyError, it should send the error back to the coding agent so it can fix the problem automatically. You can find an example to help you start this project here.

2. Text-to-SQL App

Business teams often need data but don’t know SQL, so they submit Jira tickets to the data team. This creates big bottlenecks.

You can build an app that takes a natural language question, like “What were the top-selling products in the Midwest last quarter?”, turns it into a correct SQL query, runs it on a database, and shows the results in a clear table.

For an LLM, writing the SQL is easy. The real challenge is giving it the right context. For example, if your database has a column called rev_q3_fnl, the LLM won’t know that means “Q3 Revenue.” You’ll need to build a system that adds database schemas and data dictionaries to the prompt automatically. A project that can handle complex table joins and schema context will help you stand out. You can find an example to get started with this project here.

3. Multi-Document RAG System

Standard Retrieval-Augmented Generation (RAG) on a single PDF is simple. But in real situations, data is spread across many sources. A company might need answers that combine information from a large SQL table, a folder of PDF reports, and a JSON API feed.

Try building a routing RAG system that can work with different types of data. When someone asks a question, the system should figure out what they want, choose the right data source, pull out the needed information or run a query, and then put together a clear answer.

The key to a good production RAG system is how you break up the data and filter by metadata. Don’t just put raw text into a vector database. Instead, pull out details like date, author, and document type, and use both keyword and vector search. Show that your system can answer questions that involve two very different documents without making things up. You can find an example to help you start this project here.

4. An End-to-End AI Data Analyst

This is the most advanced LLM data application right now. An AI Data Analyst does more than just basic EDA or SQL queries; it tries to act like a junior human analyst.

You give the system a raw dataset and a broad business question, like “Analyze this customer churn dataset and tell me why users are leaving, and what we should do about it.” The system should plan how to analyze the data, write the code, run statistical analysis, create visualizations, and put together a final business report.

A common mistake for junior developers is not setting limits for the LLM. If you let it do whatever it wants, it can get off track and use up a lot of API tokens. To build this well, make sure the LLM creates a clear Execution Plan first, check that plan with a validation step, and only then let it run the code. You can find an example to help you start this project here.

Closing Thoughts

To sum up, here are four practical, industry-focused LLM-based Data Analysis projects that can really help your resume stand out:

When working on these projects, don’t worry too much about which framework you use. Focus on how the data moves through your system. This will help you handle errors, manage context windows, and stop the system from crashing when the LLM gives you bad JSON.

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.

The post 4 LLM-Based Data Analysis Projects appeared first on AmanXai by Aman Kharwal.

Back to Listing

credit: