A Complete Architecture Guide for RAG + Agent Systems
This article provides a comprehensive guide to building stable, scalable, and debuggable RAG (Retrieval Augmented Generation) and multi-agent workflows.
Why it matters
This guide offers a comprehensive approach to building production-ready RAG and agent-based AI systems, which are becoming increasingly important for real-world applications.
Key Points
- 1Deterministic ingestion pipeline to prevent content drift
- 2Retrieval drift mapping to identify and mitigate changes in embeddings and document updates
- 3Chunking strategies to preserve meaning and minimize boundary artifacts
- 4Debug checklist to catch common failure modes in RAG systems
- 5Evaluation pipeline to prevent regressions as the system grows
Details
The article covers 11 key components for building robust RAG and agent-based systems. It emphasizes the importance of deterministic ingestion, retrieval drift mapping, chunking strategies, comprehensive debugging, rigorous evaluation, and well-defined tool contracts. The goal is to move beyond relying solely on prompting and instead focus on building a maintainable architecture with strong verification and drift control mechanisms. The article provides technical details and best practices for each component, highlighting how they work together to create a scalable and reliable AI-powered system.
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