Build These AI Projects to Get Hired
Tech With Tim ·2026-06-17 ·1 min read
Summary written by us from the video's transcript. The video, and everything in it, is Tech With Tim's work.
Learn which AI project ideas can make your portfolio stand out and demonstrate end‑to‑end development skills.
Takeaways
- A portfolio of deployable, domain‑specific AI projects differentiates you from most candidates.
- Building a RAG chatbot teaches data retrieval, prompt design, and reliable integration.
- An AI agent that uses tools demonstrates practical automation and interaction with external services.
- Semantic search projects highlight the ability to apply LLMs for meaning‑based information retrieval.
- Clear documentation and live demos are essential for showcasing end‑to‑end development skills.
Why a Focused Portfolio Matters
Having deployable projects on GitHub puts you ahead of most applicants and shows you can ship something from start to finish.
The goal is to avoid generic tutorial copies and instead build real‑world solutions that solve specific problems.
RAG Chatbot for a Specific Domain
Create a Retrieval‑Augmented Generation (RAG) chatbot that answers questions within a narrow domain.
Although the concept sounds simple, making it reliable in production requires handling data retrieval, prompt engineering, and integration challenges.
AI Agent with Tool Use
Develop an autonomous agent capable of using external tools such as web browsers, APIs, or file systems.
The agent should be able to browse the internet, call APIs, create or modify files, and interact with its environment to accomplish tasks.
Semantic Search System
Build a search tool that leverages large language models to understand meaning rather than just keyword matching.
Apply it to any sizable dataset—product listings, articles, job postings—to retrieve results based on semantic relevance.
Putting It All Together
Deploy each project so reviewers can test them live; include clear README instructions and demo links.
Document the problem you’re solving, your architecture choices, and any challenges you overcame to showcase end‑to‑end capability.