Taking RAG to Production — Evals, Observability, Security, and Beyond (Introduction)

About This Guide

In the previous guide, Building a RAG System from Scratch with pgvector and Gemini, we implemented a RAG system from scratch using pgvector and Gemini, then extended it through Tool Use, AI Agents, MCP, and cloud deployment.

This guide is the sequel. It takes you from "building a system that works" to "making a system that works in production".

[Previous Guide]
RAG → Tool Use → AI Agents → MCP → Render × Supabase deployment

[This Guide]
Evals → Observability → Security → MLOps → Fine-tuning
→ Multi-Agent → Governance

Why Production Operations Are Hard

After implementing RAG or Agents, you'll inevitably hit these problems when trying to go live:

Quality problems
Manually checking "is this answer correct?" doesn't scale. You need an automated system to measure quality.

Visibility problems
When something goes wrong in production, you can't diagnose it if you can't track "what happened at which step."

Security problems
When accepting requests from external users, you need to handle prompt injection attacks and prohibited content.

Continuous improvement problems
If you have no way to verify "did this actually get better?" after improving a prompt, your iteration cycle stalls.

Model problems
Sometimes a general-purpose model isn't enough — you need a model specialized for your specific domain.

Guide Structure

Each chapter can be read independently. The content assumes the previous guide's implementation (pgvector, Gemini, RAG), but you can read for conceptual understanding alone.

Prerequisites

• Completed the previous guide's pgvector tutorial
• Python 3.11, Docker, pgvector environment set up
• GEMINI_API_KEY configured in .env

Tools Used

Let's start with Chapter 2: Evals.