How to Build an LLM Evaluation Platform with Amazon Bedrock | Production AI Quality

Building an AI application is easy. But how do you prove that it is accurate, relevant, grounded, safe, and ready for production? In this KnowledgeBytes episode, I demonstrate a complete LLM Evaluation Platform built with Amazon Bedrock and show how we use it to continuously measure and improve AI quality. This is not just an architecture discussion — you'll see the working implementation, including the evaluation harness, golden datasets, independent Bedrock judge, root-cause diagnostics, baseline comparison, quality gates, and the live KnowledgeBytes AI Quality Dashboard. 00:00 Introduction – Can You Trust Your AI? 01:32 Why Traditional LLM Testing Isn’t Enough 03:42 What Should We Evaluate in Generative AI? 06:10 LLM Evaluation Harness Overview 08:55 Amazon Bedrock Evaluation Architecture 12:05 Demo – Golden Dataset & Evaluation Run 16:20 Independent Bedrock Judge Explained 18:48 Demo – LLM Judge Scores & Evaluation Results 22:10 Root-Cause Diagnostics – Why Did the AI Fail? 25:05 Quality Gates for Production AI 27:25 Demo – Baseline Comparison & Release Decision 30:25 Secure AWS to KnowledgeBytes Integration 32:20 Demo – Live AI Quality Dashboard 36:05 Build → Evaluate → Diagnose → Compare → Release 37:15 Final Thoughts What you'll learn 🔹 Why traditional software testing isn't enough for Generative AI 🔹 How to build golden datasets for repeatable LLM testing 🔹 How to evaluate correctness, completeness, relevance, faithfulness, helpfulness, and safety 🔹 How an independent Amazon Bedrock model can act as an LLM judge 🔹 How to identify root causes behind AI failures 🔹 How to detect regressions by comparing against a baseline 🔹 How to enforce AI quality gates before release 🔹 How evaluation can become part of your CI/CD process 🔹 How to securely publish sanitized AI quality metrics to a dashboard 🔹 How to move from manual prompt testing to continuous AI quality engineering The evaluation lifecycle BUILD → EVALUATE → DIAGNOSE → COMPARE → RELEASE The goal isn't simply to ask: “Does the AI work?” The better question is: “Do we have enough evidence to trust this version in production?” In the KnowledgeBytes implementation, detailed evaluation artifacts remain private in AWS, while sanitized quality summaries are published to the AI Quality Dashboard. Explore the live dashboard: KnowledgeBytes AI Quality Dashboard Learn more at: KnowledgeBytes Technologies & Concepts Amazon Bedrock • Generative AI • LLM Evaluation • LLM-as-a-Judge • AI Quality Engineering • RAG Evaluation • Golden Datasets • Root-Cause Analysis • AI Quality Gates • Regression Testing • AWS • CI/CD • Production AI Hashtags #AmazonBedrock #AWS #GenerativeAI #LLM #AIEvaluation #AIEngineering #RAG #MachineLearning #KnowledgeBytes #ProductionAI