What is RAG in Recruiting? How AI Actually Reads Your CVs

If you've looked at AI recruiting tools recently, you've probably seen phrases like "AI-powered matching," "semantic search," or "LLM-based screening." You may also have heard the term RAG — Retrieval-Augmented Generation — and wondered what it actually means and why it matters.

This guide explains RAG in plain English, why it's better than the keyword matching that most ATS tools call "AI," and how it works in practice when you're screening CVs.

The problem RAG solves

Imagine you have 200 CVs and a job description for a Senior Backend Engineer. You want to find the best 10 candidates.

The old way (keyword matching): The system searches for words from your JD in each CV. CVs that contain "Python," "REST API," "PostgreSQL" score higher. CVs that don't use those exact words score lower — even if the candidate has 8 years of Python experience described differently.

This approach has two big problems:

1. Gaming: Candidates who know about ATS systems stuff their CVs with keywords. Keyword density beats actual experience.
2. Context blindness: The system has no idea what the words mean. "Managed a team" is treated the same whether the team was 2 people for 3 months or 20 people for 5 years.

What you actually want: A system that reads each CV the way a senior recruiter would — understanding what the candidate has actually done, how it compares to what you need, and where the gaps are.

That's what RAG enables.

What RAG stands for

Retrieval-Augmented Generation is a technique that combines two AI capabilities:

1. Retrieval — finding the relevant information from a large dataset (your CV library)
2. Generation — using a large language model (LLM) to reason over that information and produce a useful output

In the context of recruiting, RAG works like this:

1. Your CVs are embedded — each CV is converted into a numerical representation (called a vector embedding) that captures its meaning. Similar CVs end up close together in this numerical space. A CV about Python backend engineering and a CV about Node.js backend engineering end up closer to each other than either is to a CV about graphic design.

2. Your job description is embedded — the JD is converted into the same numerical space.

3. Retrieval — the system finds the CVs whose embeddings are closest to the JD embedding. These are the semantically similar candidates — people who do similar work to what you're hiring for, even if they use different words.

4. Generation — a language model reads the retrieved CVs and the JD, and generates a human-readable analysis: hire recommendation, matched skills, experience fit score, gap analysis.

The result is a ranking system that understands what your candidates have done and whether it matches what you need — not just whether they used the right words.

Why this matters in practice

Here's a concrete example. You're hiring a backend engineer and you write this in your JD:

"5+ years building scalable APIs and microservices. Experience with PostgreSQL and Redis. Worked in a fast-paced startup environment."

Keyword matching would look for: "scalable APIs," "microservices," "PostgreSQL," "Redis," "startup."

RAG-based matching understands that:

• A candidate with "built distributed systems at high-growth B2B SaaS companies" matches the startup/scalable intent
• "RDBMS experience" and "caching layer" maps to PostgreSQL + Redis conceptually
• A candidate from a 30-person Series A startup who built payment systems matches "fast-paced startup" better than someone from a Fortune 500 who built internal tools

The AI doesn't just count words — it understands what you're looking for.

How Rekvo uses RAG

When you upload CVs to Rekvo:

1. Each CV is parsed and embedded using a language model
2. The embedding is stored in a vector database (Rekvo uses Qdrant, a purpose-built vector search engine)
3. Each company's embeddings are stored in a completely isolated collection — your data is never mixed with another company's

When you run an AI match:

1. Your job description is embedded
2. The vector database retrieves the closest-matching CVs (retrieval step)
3. An LLM reads the retrieved CVs and the JD, and generates a detailed assessment for each candidate (generation step)
4. Results are ranked and returned with: hire recommendation, matched skills, experience fit score, and gap analysis

When you use conversational search ("Who has experience with Kubernetes and has a background in fintech?"):

1. Your question is embedded
2. Relevant CVs are retrieved
3. The LLM reads the question and the retrieved CVs and answers in natural language

This is RAG in action — the retrieval finds the right candidates, the generation turns raw CV text into actionable insight.

RAG vs keyword search: the practical difference

| | Keyword search | RAG | |---|---|---| | Finds "Node.js" when JD says "JavaScript runtime" | ❌ | ✅ | | Distinguishes 2-year vs 8-year experience | ❌ | ✅ | | Understands "startup experience" as context | ❌ | ✅ | | Resistant to keyword stuffing | ❌ | ✅ | | Can answer questions like "who has led a team?" | ❌ | ✅ | | Speed | Fast | Fast (1–2 min for 200 CVs) |

What RAG still can't do

RAG-based screening is a significant improvement over keyword matching, but it's not magic:

• It takes CVs at face value. If a candidate lists experience they don't have, the AI won't know. References and interviews still matter.
• It works within the data you give it. A CV that's poorly written or structured will be harder to parse accurately than a well-structured one.
• It can't assess culture fit, communication skills, or attitude. These require human judgement.

RAG solves the filtering problem — reading 200 CVs to find the best 10 — so you can spend your human judgement on the evaluation problem: interviewing the right candidates.

For a practical step-by-step guide to using AI screening in your workflow (without needing to understand the technology), see How to Use AI for Candidate Screening.

FAQ

Do I need to understand RAG to use Rekvo? No. You upload CVs, post a job, and click match. The RAG architecture is what makes the results accurate — you don't need to know how it works to benefit from it.

Is my candidate data safe? Yes. Each company's CV embeddings are stored in a completely isolated vector collection. Your data is never used to train models or accessed by other companies.

What if a CV is in a language other than English? Rekvo's underlying language model supports multiple languages. CV matching works best in English but handles other major European languages reasonably well.

Looking for an ATS that uses RAG-based matching? See Best ATS for Startups in 2026 for a full comparison.

Try Rekvo's AI matching free — results in under 2 minutes.