Becoming a beginner again: Building a Production-Grade RAG MVP with Claude AI

2026-07-12 · Source: AI on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The article describes building a production-grade Retrieval-Augmented Generation (RAG) MVP using Claude AI as a pair-programming partner. This project tackles the complex challenge of text-plus-tabular retrieval from spreadsheets and PDFs, moving beyond simpler plain-text RAG. The development follows a 15-stage software development lifecycle, with the first four stages completed. A core principle is adhering to "real engineering discipline," which includes independent verification of each stage, stage-by-stage documentation, and a constraint to run on an ordinary Windows laptop without a dedicated GPU. Challenges encountered included disk space issues within Claude's sandbox and the necessity for robust testing beyond just green test suites. By Stage 4, the system achieved 100 passing tests with 99% coverage, producing correctly-shaped 768-dimension vectors and effective retrieval ranking, all locally and open-source.

Key takeaway

For AI Engineers or ML Engineers building RAG systems, especially with AI coding agents, you should prioritize rigorous, independent verification at every development stage. Do not solely rely on green test suites; instead, confirm actual behavior with human checks and analyze the agent's full reasoning trace to uncover hidden environmental or design flaws. This approach ensures robust, production-grade systems, particularly when handling complex data like tabular PDFs.

Key insights

Building production-grade RAG with an AI agent requires strict engineering discipline and independent verification, especially for complex data.

Principles

• Green tests don't guarantee correct behavior.
• AI agent reasoning traces reveal hidden issues.
• Direct agents with outcome-based delegation.

Method

The project follows a 15-stage software development lifecycle, building RAG components (loaders, chunking, embedding) sequentially. Each stage is independently verified, documented, and tested against real output before proceeding.

In practice

• Implement independent stage verification.
• Review AI agent's full reasoning trace.
• Test against real-world, complex data.

Topics

• Retrieval-Augmented Generation
• AI Coding Agents
• Production-Grade MVP
• Tabular Data Processing
• Software Development Lifecycle
• Claude AI

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.