AI Systemsmoderate complexity

RAG Application Blueprint

Reference architecture for retrieval-augmented generation apps - embedding pipelines, vector search, prompt orchestration, and evaluation.

Architecture

System Components

Key building blocks of this architecture, layered from infrastructure up

Document Ingestion

Crawl, parse, and chunk source documents into embedding-ready text.

Unstructured.ioPyPDFMarkdown

Embedding Pipeline

Generate embeddings with provider abstraction - see provider comparison.

OpenAI EmbeddingsCohereVoyage

Vector Store

Store and query vectors with metadata filtering.

PineconepgvectorQdrant

Retrieval Layer

Hybrid search combining semantic and keyword retrieval. See the RAG playbook.

BM25RerankingMMR

Generation Layer

LLM call with retrieved context and citation handling.

Anthropic ClaudeVercel AI SDKStreaming

Evaluation Harness

Offline and online evals for retrieval quality and generation accuracy.

RagasLLM-as-JudgeGolden Sets

Planning

Key Considerations

Important factors to keep in mind when implementing this architecture

Chunking strategy makes or breaks retrieval quality

Always cite sources - hallucinations are expensive in legal and healthcare

Cache embeddings and generation outputs to control costs

Start a project for a RAG build.

Options

Alternatives to Consider

Other approaches that might fit your specific needs

Fine-tuning instead of RAG for stable, narrow domains

LlamaIndex for opinionated RAG orchestration

Managed services like Vectorize or Mendable

Need help implementing this architecture?

I can help you adapt this blueprint to your specific requirements and guide implementation from planning through production deployment.

Discuss Your Project

AI Systems

Related Architectures

Other blueprints in this category

complex

AI Application Architecture

Architecture for production AI applications with model serving, RAG pipelines, evaluation, and cost controls.