Production RAG: From Anti-Patterns to Platform Engineering

The provided document outlines a comprehensive framework for deploying and managing Retrieval-Augmented Generation (RAG) systems in an enterprise setting. The framework is structured around five pillars, each addressing critical aspects of RAG system design and operation:

1. Retrieval & Intelligence: This pillar focuses on enhancing the precision of retrieval and ensuring that knowledge bases are up-to-date and version-controlled.
2. Action, Memory & Learning: It deals with how agents interact with external systems, manage context over time, and learn from feedback to improve performance.
3. Security, FinOps & Control: This pillar covers security measures, financial operations (FinOps), and overall control mechanisms necessary for enterprise deployment.

Detailed Breakdown

Pillar 1: Retrieval & Intelligence

• Query Rewriting: Enhances retrieval quality by generating multiple query variants that address different aspects of the original question.
• Knowledge as a Versioned Artifact: Ensures knowledge base updates are reversible and version-controlled to safeguard RAG performance.
• Automated Accuracy Gates: Implements Jenkinsfile-based gates to enforce quality thresholds before deployment.
• Intelligent Model Routing: Optimizes LLM usage by routing simple queries to less resource-intensive models, reducing costs without impacting

Read the full article at Towards AI - Medium

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