RAG System Blueprint
Retrieval-Augmented Generation: grounded answers with citations from your knowledge base. From ingestion to generation, every stage engineered for accuracy.
Six stages from data to grounded answer
Click any stage for technical depth.
Document Ingestion
Capture documents from any source with format-aware parsing.
Raw content flows in from PDFs, wikis, APIs, file stores, and webhooks. Format-aware parsers extract clean text, tables, images, and metadata. OCR handles scanned documents. Each source has a dedicated connector with incremental sync.
How you chunk determines how you retrieve
The most underestimated decision in RAG. We evaluate all four strategies against your data.
Fixed-Size Chunking
Split by character/token count with overlap. Simple and predictable.
Homogeneous documents, initial prototyping
Beyond naive RAG
Four advanced retrieval patterns for when basic “embed → search → generate” isn't enough.
HyDE
Hypothetical Document Embeddings
Generate a hypothetical answer first, embed it, and use that embedding for retrieval. Often finds more relevant chunks than the raw query.
Retrieval quality controls
Every retrieval pass is measured. If these numbers drop, the system alerts before users notice.
Precision@k
Fraction of retrieved chunks that are relevant.
Recall
Fraction of all relevant chunks retrieved.
Chunk Relevance
Semantic similarity post re-ranking.
Citation Accuracy
Claims correctly traced to sources.
Faithfulness
Generated answer consistent with context.
Answer Relevance
Answer directly addresses the query.
How we measure RAG quality
Five evaluation dimensions. Automated + human. Continuous monitoring in production.
Context Precision
Are the retrieved chunks relevant to the query?
LLM-as-judge + human annotation
Context Recall
Were all relevant chunks retrieved?
Ground-truth comparison
Faithfulness
Is the answer supported by the retrieved context?
Claim decomposition + entailment check
Answer Relevance
Does the answer address the question?
Semantic similarity + LLM evaluation
Hallucination Rate
Does the answer contain unsupported claims?
NLI model + citation verification
“No source, no claim.”
If the retriever cannot surface a supporting document, the model is instructed to say so — never to fabricate. Every claim in the generated response traces back to a cited source chunk.
Knowledge lifecycle management
Knowledge is not static. The system detects change, re-indexes, and keeps answers fresh.
Incremental Indexing
New documents are chunked, embedded, and indexed without rebuilding the full store. Sub-minute latency for fresh knowledge.
Re-embedding Triggers
When source content changes beyond a diff threshold, affected chunks are automatically re-embedded and swapped in.
Versioned Knowledge
Every chunk carries a version stamp. Roll back to any point-in-time snapshot of your knowledge base for audit or comparison.
Stale Content Detection
Automated freshness scoring flags chunks whose source documents have been updated, deleted, or exceed a TTL window.
Access-Tier Scoping
Chunks inherit access permissions from source documents. Retrieval respects user roles — no unauthorized knowledge leaks.
Multi-Tenant Isolation
Each tenant has logically isolated vector namespaces. No cross-contamination of knowledge between organizations.
Production deployment architecture
A horizontally scalable architecture designed for enterprise workloads.
Ingestion Workers
Distributed workers for parallel document processing
Embedding Service
GPU-accelerated embedding with request batching
Vector Database
Sharded vector store with read replicas
Retrieval API
Stateless retrieval service with caching
Generation Service
LLM inference with streaming response
Monitoring Stack
Metrics, alerting, and quality tracking
Tell the AI where your knowledge lives.
Describe your data sources and we'll map this blueprint to your environment.