Build a Production-Grade RAG Pipeline | Knowledge Layer in AI Solution Architecture скачать в хорошем качестве

Build a Production-Grade RAG Pipeline | Knowledge Layer in AI Solution Architecture

In this episode of the AI Solution Architecture series, I break down how to design a production-grade RAG pipeline — from data ingestion to grounded LLM answers. We cover: • Batch, streaming, and real-time ingestion patterns • Chunking strategies (fixed, semantic, sliding window, hierarchical) • Optimal chunk size (why 512 tokens is the sweet spot) • Embedding model selection (OpenAI, Cohere, OSS) • Vector indexing and metadata filtering • RAG architecture flow (query → embed → retrieve → rerank → generate) • Retrieval and generation evaluation metrics • Data quality framework for knowledge bases • Governance, compliance, and access control in RAG systems If you’re building AI systems for production — not demos — this layer determines accuracy, cost, compliance, and trust. This is Layer 2 of the AI Solution Architecture framework. 🎬 Previous Episodes: Episode 0 – Why AI Systems Fail in Production    • AI Infrastructure for Production Systems: ...   Episode 1 – Foundation Layer: Infrastructure Decisions    • AI Solution Architecture: 6 Core Layers Th...   📌 Full AI Solution Architecture Playlist:    • AI Solution Architecture — From Demo to Pr...   If you're serious about enterprise AI maturity, subscribe and follow the full architecture series. — Ahmed Mahmoud AI Solution Architect | Founder, DataMindAI