Build a Document Q&A System with RAG
One of the most in-demand AI skills in 2026 is Retrieval-Augmented Generation (RAG). This technique connects a Large Language Model (LLM) to a private data source, making its answers more accurate and context-specific. Build an application where a user
can upload a document, like a PDF report or company handbook, and ask questions about its contents. This project demonstrates that you understand core concepts like document chunking, embeddings, vector search, and controlling model hallucinations. Recruiters care because nearly every company has internal documents they want to make searchable and intelligent. Showcasing a project like this proves you can solve a real-world business problem.
Create a Domain-Specific AI Assistant
Generic chatbots are common, but niche assistants that solve a specific industry's problem are far more impressive. Consider building an AI assistant for a field you're passionate about, such as a legal-tech tool that parses contracts or a healthcare bot that helps schedule appointments. This approach shows recruiters you can apply AI in a targeted, practical context. Fine-tuning an open-source model on a specialised dataset demonstrates a deeper level of skill than just using a standard API. It proves you can think about a user's specific needs and tailor a solution, which is a highly valued skill for any product-focused team.
Develop an AI Agent That Takes Action
Go beyond text generation and build an AI agent that can perform tasks. This is a fast-growing area in AI engineering. An agent can be given a goal and then use tools—like calling an API or browsing the web—to achieve it. For example, you could build an agent that researches a topic from multiple sources and provides a synthesized summary, or one that automates booking appointments. This type of project signals that you understand how to build more complex, autonomous systems that can handle multi-step reasoning and recover from errors. It moves you from someone who just uses AI to someone who orchestrates it.
Ship an End-to-End Deployed Application
Perhaps the most critical project is one that is fully complete and deployed. Many portfolios are full of Jupyter notebooks, but very few show a finished product that real users can interact with. Take one of your models—whether it's a recommendation system, an image classifier, or your RAG chatbot—and build a full application around it. This means creating a user interface, deploying it to a cloud service, and making it accessible via a public URL. This is the crown jewel of a portfolio because it proves you don't just build models; you ship products. It shows a hiring manager you can handle the entire project lifecycle, from raw data to a live application.
Showcasing Your Work Effectively
Building the projects is only half the battle. How you present them is what gets you the interview. For each project in your portfolio, include a clear README file on GitHub that explains the problem you solved, the technologies you used, and the outcome. Provide a live demo link whenever possible. Even better, record a short video where you walk through the project and explain your design choices. Recruiters spend only a few seconds on each portfolio, so making your work easy to understand and appreciate is crucial for turning a glance into a conversation.
