How RAG Works

A technical deep-dive into retrieval-augmented generation architecture, from data ingestion to continuous improvement.

01

Data Ingestion

Your documents enter the RAG pipeline through secure ingestion endpoints.

•Supported formats: PDFs, Word documents, Excel spreadsheets, emails, database exports
•OCR processing for scanned documents and images
•Intelligent chunking strategies: semantic boundaries, token limits, overlap windows
•Metadata enrichment: document type, creation date, author, department tags
•Data validation and sanitization before processing

02

Vectorization

Text chunks are converted into high-dimensional vectors that capture semantic meaning.

•Embedding model abstraction: supports OpenAI, Cohere, open-source models (e.g., sentence-transformers)
•pgvector schema: efficient storage and indexing in PostgreSQL
•Namespaced per client: complete data isolation in multi-tenant deployments
•Batch processing for large document sets
•Embedding versioning for model upgrades

03

Retrieval

When a query arrives, the system searches for the most relevant document chunks.

•Hybrid search: combines semantic (vector) and keyword (BM25) matching
•Top-k tuning: configurable retrieval count based on use case (typically 5-20 chunks)
•Re-ranking: secondary scoring to improve relevance (e.g., cross-encoder models)
•Query expansion: synonym handling and domain-specific term mapping
•Filtering: metadata-based constraints (date ranges, document types, departments)

04

Generation

Retrieved context is injected into the LLM prompt, which generates a source-grounded answer.

•Prompt injection with retrieved context: structured templates ensure proper context placement
•Guardrails: output validation, toxicity filtering, fact-checking against sources
•Citations mandatory: every claim links back to source documents with page numbers
•Confidence scoring: indicates answer reliability based on source quality and relevance
•Streaming responses for real-time user experience

05

Continuous Improvement

Feedback loops and analytics drive ongoing optimization of retrieval and generation.

•Feedback loops: user ratings, correction submissions, false positive tracking
•Answer scoring: metrics for accuracy, relevance, and user satisfaction
•Re-embedding pipeline: periodic updates when documents change or models improve
•A/B testing: compare embedding models, retrieval strategies, and prompt templates
•Analytics dashboard: query patterns, retrieval performance, answer quality trends

Ready to Build Your RAG System?

Every RAG implementation is unique. We'll design an architecture that matches your data, compliance requirements, and accuracy needs.