Hire Retrieval-Augmented Generation (RAG) Developers

RAG developers: retrieval quality is the whole product

Most retrieval-augmented generation systems fail at retrieval, not generation. The model is rarely the problem; what reaches it is. Buyers hiring RAG developers tend to evaluate candidates on their familiarity with a vector database and a framework, which says little about whether they can make a search over ten thousand messy PDFs return the right passage. The other common error is expecting a quick prototype to behave like a production system, when the gap between the two is almost entirely about retrieval evaluation, document handling and access control. Hyparz vets RAG developers on retrieval engineering first (about 5% of AI applicants pass) and, as of October 2026, has 4 vetted RAG engineers available; matching typically takes days, with a one-week risk-free trial on your real documents.

Inside a production retrieval pipeline

Senior RAG work starts with ingestion: parsing PDFs, tables, HTML and tickets into clean text with metadata, chunking by structure rather than fixed length, and keeping chunks tied to their source and permissions. Retrieval is usually hybrid, combining embeddings with keyword search such as BM25, followed by a reranker, with query rewriting for vague questions. Storage can be pgvector, Qdrant, Weaviate, Elasticsearch or a managed service; the choice matters less than the evaluation set of real questions with known correct passages that drives every tuning decision. Citations, freshness and cost close the loop.

Where RAG fits, and when it is the wrong tool

RAG is the right approach when answers must come from a changing body of your own material: policies, manuals, contracts, support history, product catalogues or internal wikis. It keeps the model grounded, makes answers citable, and avoids retraining when documents change. It is a poor fit for numerical analysis over structured data, where a query against a database is more reliable, and for tasks needing a specific writing style, where fine-tuning helps more. Many strong systems route between retrieval, database queries and tools rather than forcing everything through a single vector search.

Our RAG screen: messy documents and hard questions

Candidates get a small corpus with the usual faults, such as scanned pages, duplicated versions and tables, plus a question set where several answers depend on the right document version. We score retrieval recall and precision before looking at generated answers, review how they chunk and enrich documents, and ask how they would enforce per-user document permissions, handle updates and measure hallucination. Then we discuss trade-offs between recall, latency and cost. Developers who optimise prompts before fixing retrieval do not pass.