If your résumé is light on AI experience, the fastest way to fix that isn’t a certificate — it’s a working app. Retrieval-augmented generation (RAG) is the most in-demand pattern in AI engineering right now, and you can build a real one in a weekend. This guide walks you through it milestone by milestone, and gives you the résumé bullets you’ll have honestly earned.
What you’ll build: a Q&A app that answers questions about your own documents — it splits them into chunks, embeds them into a vector store, retrieves the relevant pieces for a question, and asks an LLM to answer using only those sources. Embeddings + retrieval + LLM = RAG.
Every company adding AI to their product needs people who understand retrieval, embeddings, and grounding an LLM in real data — not just calling a chat API. A deployed RAG app proves you can do the thing the job actually requires. It maps directly to keywords recruiters search for: RAG, embeddings, vector database, LLM, prompt engineering, Python.
Skills & keywords you’ll demonstrate
Python, REST API (FastAPI or Flask)
Embeddings & semantic search
A vector store (pgvector, Chroma, or FAISS)
LLM integration & prompt assembly
Chunking, retrieval, and grounded answering
Starter repo
Clone github.com/OptimalMatch/resume-project-rag-qa — it has the folder structure, a requirements file, and a checklist. Build it under your own GitHub account and commit at each milestone, so your repo shows real progress (recruiters look at commit history).
Build it in milestones
Ingest documents. Load a few PDFs or markdown files and split them into ~500-token chunks with overlap. Commit.
Embed & store. Generate an embedding for each chunk and store it in a vector store with its source text. Commit.
Retrieve. For a question, embed it and pull the top-k most similar chunks. Print them so you can see retrieval working. Commit.
Answer. Build a prompt that gives the LLM the question + the retrieved chunks and instructs it to answer only from those sources (and say “I don’t know” otherwise). Commit.
API + UI. Wrap it in a small FastAPI endpoint and a minimal web page (or a Streamlit app). Commit.
Cite sources & deploy. Return which chunks the answer came from, and deploy to a free host. Commit + add the live URL to your README.
Stretch goals
Hybrid retrieval (keyword + vector) and re-ranking.
An evaluation script that scores answer quality on a few test questions.
Swap in a different model and compare cost/quality.
Put it on your résumé
Once it’s built and deployed, these are honest, specific bullets:
“Built and deployed a retrieval-augmented-generation (RAG) Q&A app in Python (FastAPI) — chunking, embeddings, a pgvector store, and grounded LLM answers with source citations.”
“Implemented top-k semantic retrieval and an evaluation harness to measure answer accuracy across a test set.”
Add the project (and its GitHub + live link) to your résumé, then run it through the free ATS resume score to see how much your match jumps for AI roles. The Claude Code workflow can even help you tailor and apply.
Frequently asked questions
Do I need a paid AI API to build this? No. You can build the whole pipeline against a free local embedding model and a small open LLM, or a free API tier. The skills — chunking, embeddings, vector search, prompt assembly — are the same regardless of which model you plug in.
Is a RAG app impressive on a junior résumé? Yes. RAG is one of the most in-demand patterns in AI engineering right now, and a working, deployed RAG app shows you understand embeddings, retrieval, and LLM integration — far more than a tutorial certificate.