Hire a Vetted RAG Engineer

What a RAG engineer actually does

RAG — retrieval-augmented generation — is the technique that lets an AI assistant answer questions from your documents instead of its training data. A RAG engineer builds that pipeline end to end: ingesting your files (contracts, SOPs, manuals, tickets), chunking and indexing them so the right passages can be found, wiring retrieval into the model's answers, and attaching citations so every claim can be checked against the source.

The unglamorous majority of the work is evaluation. Anyone can wire a vector database to a model in an afternoon; the engineering is in proving, with a fixed test set of real questions, that answers are correct, grounded, and say "I don't know" when the documents don't contain the answer.

When a small business needs one

• Institutional knowledge lives in one veteran employee's head and a thousand unsearchable PDFs.
• Staff spend real time hunting through folders for policy, pricing, or procedure answers.
• Customer answers exist in your help center, but nobody — including customers — can find them.
• Compliance requires citing the exact source document for answers.

How to evaluate one: seven interview questions that work

1. "How do you measure whether the system's answers are actually correct?" The only good answer involves a golden question set built with you before launch, graded for correctness and groundedness, with a pass bar agreed in writing. "We'll iterate based on feedback" means you are the test set.

2. "What happens when the answer isn't in our documents?" Strong: explicit refusal behavior — the assistant says it doesn't know and points to a human. Weak: the model "usually" gets it right anyway.

3. "How do you decide how to split and index our documents?" Listen for chunking strategy tied to document structure (sections, tables, headers), not a single default splitter applied to everything.

4. "Our contracts contain tables and scanned pages. How do you handle those?" Good candidates discuss table extraction and OCR quality honestly — including flagging documents that need a human-review path.

5. "How will citations work?" Every answer should link the passages it used. If the candidate treats citations as optional polish, keep looking.

6. "How do updates flow in when we change a policy document?" You want an ingestion pipeline with re-indexing, not a one-time upload that silently goes stale.

7. "What's your fallback when retrieval quality is poor for a query type?" Honest engineers describe query rewriting, hybrid search, or scoping the product down — not just "a bigger model."

Red flags

• Can't name a concrete evaluation method beyond "it looked good."
• Promises 100% accuracy.
• No plan for permissions (who can ask about which documents).
• Treats stale-content handling as out of scope.

How we remove the hiring risk

You don't interview anyone or judge this alone. Engineers in our private network are reviewed for production experience, technical judgment, communication, and the ability to own a focused project end to end. After a free assessment we write a fixed-price scope with plain-language acceptance criteria, then match the project with one vetted engineer and prepare one complete delivery plan for your approval. You pay one milestone at a time before that phase begins, the engineer is paid after you accept the milestone, and a 30-day defect warranty covers the launch.