System Design
RAG Pipeline Architecture.
How I build production retrieval-augmented generation systems. Click any node to explore the implementation details, or watch the data flow automatically.
User
Query Input
API Gateway
Request Orchestration
Embedding Model
Semantic Encoding
Vector Database
Similarity Search
Retriever
Context Assembly
LLM
Generation Engine
Streaming Response
Real-Time Output
Latency
< 200ms
P95 retrieval + generation with streaming first-token delivery
Throughput
10K+ RPM
Horizontal scaling with async pipelines and connection pooling
Accuracy
95%+
Grounded responses with hybrid search, reranking, and citation validation
Streaming Response — token-by-token
Design Principles.
Modularity
Each pipeline stage is independently deployable and testable. Swap embedding models or vector DBs without rewriting the system.
Observability
Every retrieval and generation step is traced. Latency, relevance scores, and token usage are logged for continuous improvement.
Graceful Degradation
If the retriever returns low-confidence results, the system falls back to the LLM's parametric knowledge with clear attribution.
Evaluation-Driven
Automated evals with RAGAS metrics (faithfulness, answer relevancy, context precision) run on every pipeline change.