What is RAG development?
Retrieval-augmented generation grounds model output in selected sources retrieved at request time. A production RAG system needs ingestion ownership, access filtering, retrieval evaluation, citations, and a response policy.
Retrieval-augmented generation
Builderz designs RAG around the source owners, access rules, citations, freshness, and evaluation set. Accuracy is measured on the buyer’s representative queries; it is never published as a generic percentage.
Representative sources, questions, expected citations, and known bad answers.
Identity, role, document, and field-level access requirements.
Freshness targets plus ingestion and deletion ownership.
Retrieval, answer, abstention, latency, and cost evaluation criteria.
A labeled evaluation set that can run before every material change.
Retrieval-augmented generation grounds model output in selected sources retrieved at request time. A production RAG system needs ingestion ownership, access filtering, retrieval evaluation, citations, and a response policy.
RAG fits questions that must use changing or private source material. Fine-tuning is a different tool: it can shape model behavior or output form, but should not be used as a substitute for current governed knowledge.
Builderz uses three entry offers: Architecture and delivery sprint ($3K-$5K), Production build ($10K-$40K), Reliability and rescue sprint ($5K-$15K). The project brief determines which offer fits; the proposal then defines scope, owner, acceptance criteria, exclusions, payment schedule, and change control.
Builderz starts with the workflow, authority boundaries, failure modes, and acceptance tests. Reliability targets, security controls, support terms, and deployment constraints are written into the accepted scope rather than implied as blanket guarantees.
Bring representative documents and queries, expected citations, access rules, known bad answers, freshness requirements, and a labeled evaluation set.