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AI-Driven LLM Evaluation: Picking the right AI model

Evaluate LLMs with AI-driven methods. Master large language model evaluation, ensure model faithfulness, and boost AI reliability.

Beginner Starts May 28, 20255 enrolledmodel evaluationAI as a Judge
Free

Syllabus

Course Overview
  • Why LLM Evaluation Matters
  • Beware the Hype: Why Word-of-Mouth Isn’t Enough
  • Benchmarks
  • LLM Evaluation Pipeline
Defining Evaluation Criteria
  • Business Goals
  • Quantitative Metrics
  • Qualitative Metrics
Building Your Scoring Formula
  • Introduction
  • Normalizing Metrics to 0–1 Scale
  • Hands on: Normalize Sample Models Metrics
  • Weight Assignment
  • Hands-on: Compute Sample Scores
Hands-On: Find Your Model Candidates
  • AI Writing Assistant Project
  • Identify Your Task Types
  • Define Business Goals
  • Find the Candidates
  • Estimating Total Token Usage
  • Gather Vendor Docs and Pricing Pages
AI as Judge
  • Pipeline Architecture
  • Github Repo
  • Generating Contents
  • Analyzing the articles
  • AI as judge
  • Pull the result from API
  • Finding the Winner
Production Integration
  • Introduction
  • Live Quality Control 
  • Build the Live QC
Conclusion
  • Wrap up
  • Continuous Evaluation

Requirements

Unlock the power of AI-driven techniques to evaluate large language models (LLMs) with precision and confidence. This comprehensive course teaches you how to assess LLM performance using advanced, automated methods that go beyond traditional benchmarks.

Whether you're an AI researcher, data scientist, or machine learning engineer, you'll gain practical skills to improve model faithfulness, safety, and reliability. Learn how to detect hallucinations, measure factual consistency, and optimize LLM outputs in real-world applications.

By the end of this course, you'll know how to:

  • Apply cutting-edge LLM evaluation frameworks and tools
  • Diagnose and reduce hallucinations and biases
  • Automate evaluation workflows for scalable model testing
  • Enhance model performance using AI-assisted quality control
  • Ensure output accuracy and trustworthiness across use cases

Instructors

Amir Tadrisi

Amir Tadrisi

AI for Education Specialist

Amir is a full-stack developer with a strong focus on building modern, AI-powered educational platforms. Since 2013, he has worked extensively with Open edX, gaining deep experience in scalable learning management systems. He is the creator of Cubite.io, and publishes AI-focused learning content at The Learning Algorithm and Testdriven. His recent work centers on integrating artificial intelligence with learning tools to create more personalized and effective educational experiences.