2 posts tagged with "evaluation"

Your AI pipeline generates a parts list for a LEGO castle MOC. It says you need 12x "Brick 2 x 4" in Light Bluish Gray, 8x "Arch 1 x 4" in Dark Tan, and 4x "Slope 45 2 x 1" in Sand Green. The text looks plausible. But does the part image next to "Arch 1 x 4" actually show an arch? Does the quantity make sense for a castle build? Would this list genuinely help someone source bricks for the build?

These are multi-modal evaluation questions — they span text accuracy, image-text coherence, and practical usefulness. Standard unit tests cannot answer them. This article walks through a production evaluation pipeline built with DeepEval that evaluates AI-generated LEGO parts lists across five axes, using image metrics that most teams haven't touched yet.

The system is real. It runs in Bricks, a LEGO MOC discovery platform built with Next.js 19, LangGraph, and Neon PostgreSQL. The evaluation judge is DeepSeek — not GPT-4o — because you don't need a frontier model to grade your outputs.

Your RAG pipeline passes all 20 of your hand-written test questions. It retrieves the right context, generates grounded answers, and the demo looks great. Then it goes to production, and users start asking the 21st question — the one that exposes a retrieval gap, a hallucinated citation, or a context window that silently truncated the most relevant chunk. You had 20 tests for a knowledge base with 55 documents. That's 0.4% coverage. The other 99.6% was untested surface area.

This guide shows how to close that gap. We walk through a production implementation that generates 330+ synthetic test cases from 55 AI engineering lessons, evaluates a LangGraph-based RAG pipeline across 10+ metrics, and runs hyperparameter sweeps to find optimal retrieval configurations — all automated with DeepEval and pytest.