Technical guide

The RAG tech stack, from source data to evaluated answers

Understand the components that make retrieval-augmented generation reliable—and the tradeoffs that matter when moving from a demonstration to production.

Review your architecture

Source-connected

Permission-aware

Measured before launch

01

A RAG stack is a pipeline, not a product list

A production RAG application coordinates ingestion, storage, retrieval, generation, security, evaluation, and operations. Each layer affects the others. Poor parsing cannot be repaired by a better model; weak metadata limits filtering; missing evaluation makes tuning subjective.

The right stack is the smallest set of components that meets quality, security, latency, scale, integration, and maintainability requirements.

02

Core architecture layers

✓

Connectors and change detection for source systems

✓

Parsing, OCR, layout handling, normalization, and chunking

✓

Metadata, document identity, versions, and deletion handling

✓

Embedding models and vector indexes

✓

Keyword, semantic, hybrid, and structured retrieval

✓

Filters, rerankers, query transformation, and context assembly

✓

Language models, prompts, tools, citations, and structured output

✓

Evaluation, tracing, feedback, observability, and cost controls

03

Build-versus-buy decisions

Managed platforms can accelerate a standard use case. Custom components become valuable when permissions, retrieval behavior, domain structure, integrations, scale, or user experience are differentiating. We assess exit options, data portability, observability, and evaluation access—not only launch speed.

04

Select with evaluation, not feature matrices

Test candidate components using your documents and questions. Measure retrieval relevance, citation support, latency, cost, operational complexity, and security fit. Preserve the evaluation set so future model or vendor changes can be compared against the same baseline.

RAG development services →RAG evaluation →

05

Common technology choices by layer

Teams may use cloud AI services, commercial search platforms, open-source retrieval frameworks, relational databases with vector extensions, dedicated vector databases, model gateways, observability platforms, and custom orchestration. The relevant question is not which logo appears in the diagram; it is whether the component meets the evaluated requirement and can be operated responsibly.

Semantic vs. vector search →RAG as a service →

Questions

Frequently asked questions

A RAG stack normally includes source connectors, parsers, metadata storage, embeddings, a search or vector index, retrieval and reranking logic, a language model, application code, evaluation, and monitoring.

No. Some use an existing search platform with vector capabilities, a relational database extension, managed retrieval, or a hybrid of structured and unstructured retrieval.

The best model depends on answer quality, context needs, latency, cost, hosting, safety, and output requirements. Evaluate models on the actual task rather than selecting from a general benchmark.

A practical next step

Bring us the workflow that is stuck—not a finished AI specification.

We will help clarify the opportunity, data, risks, and smallest useful way to prove value.

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