The Challenge
Scaling Retrieval-Augmented Generation (RAG) involves more than just connecting a vector database to an LLM. Enterprise requirements demand low latency, high relevance, and strict data privacy.
The NeuralOps Approach
This architectural study outlines our standard approach to RAG deployments, focusing on three key layers:
1. The Ingestion Pipeline
- Change Data Capture (CDC) for real-time syncing.
- Adaptive Chunking based on semantic boundaries.
- Multi-modal Embedding support.
2. The Retrieval Engine
- Hybrid Search combining dense and sparse vectors.
- Re-ranking stages to optimize for top-K precision.
- Context injection with metadata filtering.
3. The Evaluation Loop
- RAGAS scoring for faithfulness and relevance.
- Cost tracking per query.
- A/B testing of different embedding models.
This study represents our foundational blueprint for clients looking to move RAG from prototype to production.