Tutorials help you learn the basics, but they aren’t enough to land a job. There’s a big difference between a simple weekend project and a real, production-level Generative AI system. To stand out in today’s Data Science and AI Engineering job market, you need to show you understand real-world challenges. In this article, I’ll share four production-grade GenAI project ideas you should try.
Production-Grade GenAI Project Ideas
Here are four production-grade GenAI projects that will help you tackle the real challenges professionals face in the industry every day.
Build a Production-Ready LLM API
When building an AI feature, you usually don’t let the frontend connect directly to OpenAI or Anthropic. Instead, you need a gateway that manages routing, retries, cost tracking, and handling multiple requests at once. Serving an LLM well means managing the request load so users aren’t stuck waiting for results.
Don’t just put an API key in a basic Flask route. Build an API gateway that can handle real traffic. FastAPI is a good choice for fast, asynchronous routing. If you’re using local or open-weight models like Llama 3 or DeepSeek, you can try vLLM.
A strong API with proper timeout and backup logic is what makes your project more than just a toy. Here’s an example to help you get started.
Build a Text-to-SQL App
Turning a user’s natural-language question into a SQL query might sound easy, but LLMs often struggle with it. They don’t know your database structure, don’t understand your business rules, and sometimes make up tables that aren’t there.
You need to build a system that gives the LLM three kinds of context: schema context, business context, and examples of past successful queries (usage context). You can use standard RAG to get table information, or try the new Model Context Protocol (MCP) so your AI agent can securely pull schema data when needed.
LLMs handle SQL syntax well, but they struggle with your company’s unique data quirks. Focus most of your effort on building a strong metadata layer and prompt pipeline, and spend less time on the LLM generation itself. Here’s an example to help you get started.
Building a Multi-Document RAG System
Most RAG tutorials tell you to split a PDF into 500-word chunks, embed them, and search. But in real life, this often doesn’t work. A company’s knowledge base has structured policies, step-by-step manuals, and unstructured meeting notes. If you split a Q&A document carelessly, you lose important context.
You need a system that understands what it’s reading and splits the data the right way. LlamaIndex can help with smart data ingestion, and a modern vector database like Qdrant supports hybrid search by combining semantic vector search with keyword or metadata filtering.
I’ve seen teams spend weeks fine-tuning embedding models because their RAG system gave poor results.The real problem was their chunking strategy. Fix your data ingestion first. Here’s an example to help you start this project.
Build an AI Data Analyst
Users want more than just chatting with their CSV files. They want actionable insights, charts, and cleaned data. An AI Data Analyst is an agentic system that can write code, run it to analyze data, read the output, and create a final report.
Instead of relying only on an LLM’s reasoning to do math, which it often struggles with, give the LLM tools to write and run Python code. Use an agentic framework like LangGraph or CrewAI.
Trust is the hardest thing to build in AI. Always design your agent to show its work. If it creates a chart, show the exact Python code it used so a human data scientist can check it. Here’s an example to help you start this project.
Closing Thoughts
Here are four production-grade GenAI projects you should build:
- Build a Production-Ready LLM API
- Build a Text-to-SQL App
- Building a Multi-Document RAG System
- Build an AI Data Analyst
Building these four projects will help you think like an engineer and give you the practical experience that hiring managers want.
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.
