May 5
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Here's the pattern I see over and over: a team picks Claude or Llama because a benchmark said it was good, builds an entire application around it, ships to production — and then discovers the model hallucinates on their specific domain, or the tone doesn't match their brand, or the RAG pipeline retrieves irrelevant chunks. By then, switching models means rewriting everything. Bedrock Evaluations exists so you never end up there. It lets you test models, compare them side by side, score your RAG pipeline, and validate quality — BEFORE you commit. Automatically with algorithms, with an LLM judging another LLM, or with real humans rating responses. All from the Bedrock console. And with AgentCore Evaluations (GA as of March 2026), you can now evaluate your agents too — not just model outputs, but the full agentic loop: did it call the right tools, in the right order, and arrive at the right answer? In this video, I cover: → Why model selection without evaluation is gambling — the real risk → Three evaluation methods — Automatic (algorithms), LLM-as-a-Judge, and Human → Automatic evaluation — BERT Score, F1, exact match, semantic robustness → LLM-as-a-Judge — correctness, completeness, faithfulness, harmfulness → Human evaluation — your team or AWS-managed evaluators, custom metrics → Bringing your own dataset vs using built-in prompt datasets → Comparing models — run the same dataset against Claude, Llama, Titan side by side → RAG retrieval evaluations — is your Knowledge Base retrieving the right chunks? → RAG generate evaluations — are the generated answers correct, complete, grounded? → Custom RAG evaluation — bring your own inference responses from any RAG system → AgentCore Evaluations — ground truth, behavioral assertions, expected tool sequences → Custom evaluators — LLM-based with your own prompts, or code-based via Lambda → Console walkthrough — creating an evaluation job end to end → Pricing — you pay for inference during eval, automatic scoring is free, human tasks $0.21 each → When to evaluate — model selection, prompt iteration, RAG tuning, pre-production gate This is the "measure before you ship" discipline that separates prototype AI from production AI. If you've watched the rest of my Bedrock series, this is how you validate everything you built. ── Watch the full series ── This is part of my "AWS in Under 10 Minutes" series where I break down core AWS services from an architect's lens. Subscribe and hit the bell so you don't miss the next one. 📺 Watch the full series: https://youtube.com/playlist?list=PLnJtNg2D-JyJh78FYNTtSBa4dId9wrEen&si=3FqzGsg3qXDaPYng CONNECT WITH ME: YouTube: https://www.youtube.com/@aiwithpallavi LinkedIn: https://www.linkedin.com/in/pallavisrivastava06 Instagram: https://www.instagram.com/aiwithpallavi Topmate: https://topmate.io/aiwithpallavi