Best Generative AI Projects For Resume by DeepLearning.AI

Are you looking for the Best Generative AI Projects For Resume? If yes, then I’ve got something really useful for you.

Hi, I’m Aqsa Zafar — the founder of MLTUT and a Ph.D. scholar in Machine Learning. I love learning and sharing everything about AI and data science, especially when it can help you grow in your career.

In this post, I’ll walk you through 7 free and hands-on Generative AI projects created by DeepLearning.AI. These projects are perfect if you’re trying to start your career in AI or want to build stronger skills. You’ll get real experience working on things like chatbots, image generation, and fine-tuning models — the kind of work that actually shows up in real jobs.

Let’s go through each project and see how it can help you build a stronger AI resume.

Best Generative AI Projects For Resume

Project 1: Finetuning Large Language Models (LLMs)

This project helps you go beyond just using prompts. You’ll learn when and why finetuning is needed — and how to actually do it. This is what you’ll work on:

• Preparing your own dataset
• Training a model that fits your data
• Evaluating how well the model performs

By changing the model’s weights, you make it learn your style or specific language — which is great if you want your AI to match your brand or industry.

Skills You’ll Learn:

• How to write better prompts
• Getting your data ready for training
• Using deep learning in NLP
• Evaluating and improving model performance

Why This Project Looks Great on a Resume:
It shows you can customize a language model, not just use one. That’s a skill many companies are looking for right now.

Project 2: LangChain: Chat with Your Data

This is one of the most useful projects, especially if you want to build smart chatbots. In this project, you’ll create a chatbot that can answer questions using your own documents.

You’ll work with LangChain, a powerful tool that supports over 80 types of document formats. Along the way, you’ll explore:

• Embeddings
• Vector databases
• Retrieval-based question answering

By the end, you’ll have a chatbot that understands and responds based on your files — a big step toward building AI tools that are truly useful.

Skills You’ll Learn:

• How to use LangChain
• Loading and splitting documents
• Working with vector stores and embeddings
• Retrieval-augmented generation (RAG)

Why This Project Adds Value to Your Resume:
It shows that you can build AI systems that understand custom data, using one of today’s most important techniques — retrieval-augmented generation.

Project 3: Building Your Own Database Agent

Ever wished you could ask questions in plain English and get answers from a database? That’s exactly what you’ll build in this project.

You’ll create an AI agent that turns natural language into SQL queries using tools like:

• Azure OpenAI
• Retrieval-Augmented Generation (RAG)
• Function calling
• The Assistants API

This project is especially helpful if you work with structured data and want to make database access faster and easier.

Skills You’ll Learn:

• Converting natural language into SQL
• Using RAG with tabular data
• Integrating APIs
• Calling functions with LLMs

Why This Project Stands Out on a Resume:
It shows that you know how to connect AI with real-world data systems, which is a big plus for roles in data and software development.

Project 4: How Diffusion Models Work

If you’re curious about how AI creates images, this project is a great place to start. Instead of just using tools like Stable Diffusion, you’ll actually build your own diffusion model from scratch.

This is what you’ll do:

• Create a diffusion model step by step
• Train it on data yourself
• Make the image generation process faster
• Add your own style or conditions to the output

By the end, you’ll understand how AI generates images — not just how to use it, but how it really works behind the scenes.

Skills You’ll Learn:

• How noise prediction networks work
• Sampling methods to speed up generation
• Training a diffusion model
• Customizing image outputs with conditioning

Why This Project Looks Great on a Resume:
It shows that you understand the core of image-based generative AI — a skill that’s valuable in both creative tech roles and AI research.

Project 5: LangChain for LLM Application Development

In this project, guided by Andrew Ng and Harrison Chase, you’ll learn how to build real-world LLM applications using LangChain — one of the most powerful frameworks for working with large language models.

You’ll get hands-on with:

• Writing effective prompts
• Calling and connecting different models
• Keeping memory in conversations
• Designing smart agents that can reason

It’s a great project if you’re interested in building personal AI assistants or intelligent chatbots that can hold context.

Skills You’ll Learn:

• Creating LangChain workflows
• Prompt engineering
• Building and managing agents
• Handling memory in chat applications

Why This Project Adds Value to Your Resume:
It shows that you can build complete LLM-powered apps, not just small demos — a skill that’s in high demand across many industries.

Project 6: Functions, Tools, and Agents with LangChain

This project takes you deeper into advanced LLM workflows. You’ll learn how to build smart agents that can choose the right tools to get a task done, like tagging data, extracting info, or summarizing content.

This is what you’ll explore:

• Using LangChain Expression Language (LCEL)
• Calling functions with ChatGPT
• Routing tasks to the right tools

By the end, you’ll know how to build agents that don’t just respond — they think and choose the best tool for the job.

Skills You’ll Learn:

• Designing advanced AI agents
• Connecting tools with LLMs
• Function calling and routing
• Writing with LangChain expressions

Why This Project Looks Great on a Resume:
It shows that you understand how to build modern, tool-aware AI systems, which is a valuable skill in today’s fast-evolving AI space.

Project 7: Building Agentic RAG with LlamaIndex

This is an advanced project where you’ll build smart AI agents that can think and decide on their own. Using LlamaIndex and LangChain, you’ll create agents that can:

• Decide when to summarize or answer questions
• Use the right tools as needed
• Work across multiple documents
• Handle research-like tasks on their own

You’ll also get hands-on practice with debugging, controlling agent behavior, and building a modular system.

Skills You’ll Learn:

• How to combine LlamaIndex and LangChain
• Designing research-ready agents
• Reasoning over many documents
• Building more autonomous AI tools

Why This Project Stands Out on a Resume:
It shows you’re working with cutting-edge techniques like Agentic RAG, and you’re ready to take on real AI research and engineering challenges.

How to Start These Projects

Getting started with these projects is super easy, even if you’re new to the platform. Just follow these simple steps:

• Go to the DeepLearning.AI Generative AI Projects page
  This is where all the projects are listed. You can explore each one, read a short description, and see which skills it covers.

• Choose a project and click “Start Project.”
  Once you find a project that interests you, click the Start Project button. This will take you to the learning interface.

• Click “Continue” and then “Start Learning.”
  Don’t worry if you don’t have an account — you’ll be guided to sign in or sign up if needed. Once you’re in, click Continue and then Start Learning to begin.

• Agree to the terms and click “Launch App.”
  You’ll see a prompt to accept the platform terms. Once you agree, click Launch App — this will open the interactive workspace where you can build the project step by step.

And that’s it!
You’re now ready to start building real, hands-on AI projects — completely free, and guided by top instructors in the field. It’s a great way to learn by doing and strengthen your portfolio at the same time.

Final Thoughts

If you’ve been thinking about how to get real, practical experience in AI, these projects are a great place to start. They’re more than just tutorials — they let you build actual tools, solve real problems, and learn by doing.

Each project helps you develop skills that are highly valued in roles like:

• Machine Learning Engineer
• Data Scientist
• AI Researcher
• Prompt Engineer
• Full Stack AI Developer

If you’re serious about breaking into AI or leveling up your skills, these are some of the Best Generative AI Projects For Resume — and the best part is, they’re free to start.

Now it’s your turn to explore, build, and grow.

A Quick Tip from Me (Aqsa)

As you work on these projects, make sure to document your progress. Share your code on GitHub, write a short blog post or case study about what you learned, and explain how you applied it. This not only helps you reflect on your learning but also shows initiative — something employers really value.

These are some of the Best Generative AI Projects For Resume, so don’t keep them to yourself. If you try any of them, feel free to tag me on LinkedIn or share your experience on your blog — I’d love to see your work and support your journey.

Let’s keep learning, building, and growing — together.
— Aqsa Zafar | MLTUT

Thank YOU!

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Thought of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden