How to Hire RAG Developers - DevSocial

Hiring guide · AI and machine learning

How to hire RAG developers

This guide helps you assess a RAG Developer who can build reliable answers from company documents and databases. Use it to test retrieval quality, grounding, citations, document handling, and permission-aware access.

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Step 1 of 3

Hire RAG developers

Describe who you need, in your own words. We turn it into a short brief you can check.

What you need

300 characters left

Seniority

What good looks like at each level

Junior

Can prepare documents, create basic chunks and embeddings, connect a vector store, and implement a simple cited question-answer flow with guidance on design choices.

Mid-level

Can own a RAG feature from ingestion through evaluation, including hybrid retrieval, metadata filters, parsing failures, prompt design, and useful quality checks.

Senior

Can design reliable RAG systems across varied sources, diagnose retrieval and generation failures, enforce document permissions, and make quality tradeoffs visible to stakeholders.

Lead or architect

Can set the RAG architecture and delivery standards, align security and product needs, guide evaluation strategy, and lead a team through complex ingestion and answer-quality problems.

The interview

Questions to ask, and what a good answer covers

1
How would you design a RAG pipeline for PDFs, Office files, HTML pages, and support tickets?
A good answer covers: Covers parsing, normalization, metadata, chunking, deduplication, versioning, failed documents, and a re-indexing strategy. Distinguishes source structure and access rules rather than applying one splitter everywhere.
2
A user says answers are often missing the right passage, but the retrieved results look related. How do you debug it?
A good answer covers: Separates retrieval from generation, inspects queries and top-k results, checks chunk boundaries and metadata filters, compares dense and keyword search, and uses labeled queries to isolate the failure.
3
When would you use hybrid search and a re-ranker instead of vector search alone?
A good answer covers: Explains that embeddings handle semantic similarity while lexical search helps names, codes, and exact terms. Describes candidate retrieval, re-ranking, latency and cost tradeoffs, and measurable success criteria.
4
How would you prevent an assistant from exposing documents a user is not allowed to read?
A good answer covers: Applies identity and tenant filters at retrieval time, not just in the prompt, and discusses security trimming, authorization-source consistency, cache isolation, logging, and tests for cross-user leakage.
5
What makes a generated answer properly grounded and cited?
A good answer covers: Requires claims to be supported by retrieved passages, preserves source identifiers and page or section details, handles conflicting or missing evidence, and prefers abstention over unsupported certainty.
6
How would you evaluate a RAG system before and after a change to chunking or retrieval?
A good answer covers: Builds a representative labeled set with expected sources and answer behavior, measures retrieval recall and answer correctness separately, includes permission and abstention cases, and reviews failures qualitatively.
7
A contract search assistant gives plausible answers but cites the wrong clause. What would you investigate?
A good answer covers: Checks parsing and page mapping, chunk overlap and section metadata, retrieval ranking, prompt citation rules, and answer-to-source validation. Proposes regression cases for clause numbers, definitions, and tables.

Take-home task

Build a permission-aware cited document assistant

Given a small set of PDFs, HTML files, and JSON tickets plus sample users and permissions, build a RAG API or CLI. Return an answer, citations, and an abstention when evidence is insufficient. Hand back code, setup notes, and a few evaluation cases.

What to look for in what comes back

Clear parsing, metadata, chunking, and indexing decisions.
Retrieval that combines relevant evidence with permission filters.
Answers cite specific source locations and abstain when support is weak.
Tests or evaluation cases expose retrieval, grounding, and access-control failures.
Readable code with documented tradeoffs and reproducible setup.

Signs of a strong candidate

Thinks in separate ingestion, retrieval, generation, authorization, and evaluation stages.
Uses evidence and failure analysis rather than relying on prompt changes alone.
Treats access control, tenant isolation, and citation accuracy as core requirements.
Can explain chunking, embeddings, hybrid retrieval, and re-ranking tradeoffs clearly.
Builds evaluation sets and regression tests from real user questions.
Designs for document updates, parsing failures, observability, and operational ownership.

Warning signs

Treats a vector database as a complete RAG solution without discussing parsing, filters, or evaluation.
Relies on prompts to enforce permissions or to prevent unsupported answers.
Claims quality from a few demos without separating retrieval from generation failures.
Ignores exact terms, tables, page references, document versions, or access metadata.
Cannot explain how to handle missing, conflicting, stale, or malformed source content.

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