RAGAS-Inspired AI Evaluation Dashboard in NestJS | Evaluate RAG Systems with Faithfulness, Answer Relevancy, Context Precision & Context Recall

In this practical lesson of Phase 5 (Eval-Driven RAG Systems), we upgrade our existing Enterprise RAG Application by adding a complete AI Evaluation Dashboard inspired by RAGAS.

Instead of rebuilding the project, we extend our production-ready NestJS + Gemini + ChromaDB + Hybrid Search architecture with evaluation services, quality metrics, dataset testing, and detailed evaluation reports.

Note: Official RAGAS is a Python framework. In this video, we implement a RAGAS-inspired evaluation system in NestJS to understand the architecture and concepts. Later in the course, we'll use the official Python RAGAS library with FastAPI.

What You'll Build
AI Evaluation Dashboard
Evaluation Module
Faithfulness Score
Answer Relevancy
Context Precision
Context Recall
Overall Quality Score
Evaluation Dataset Runner
JSON Test Cases
Automated Evaluation Reports
Production-ready NestJS Architecture

Tech Stack
NestJS • TypeScript • Gemini 2.5 Flash • ChromaDB • Hybrid Search • BM25 • Claude Code • Tailwind CSS

This upgrade transforms our Enterprise RAG application into a measurable, production-ready AI system.

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