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.
About the Course
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
Course Instructors
Learn from real-world instructors with extensive experience who actively work in the roles they teach. They are committed to helping you succeed by sharing practical insights.
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.
📚 Syllabus
- 📌Why LLM Evaluation Matters
- 📌Beware the Hype: Why Word-of-Mouth Isn’t Enough
- 📌Benchmarks
- 📌LLM Evaluation Pipeline
- 📌Business Goals
- 📌Quantitative Metrics
- 📌Qualitative Metrics
- 📌Introduction
- 📌Normalizing Metrics to 0–1 Scale
- 📌Hands on: Normalize Sample Models Metrics
- 📌Weight Assignment
- 📌Hands-on: Compute Sample Scores
- 📌AI Writing Assistant Project
- 📌Identify Your Task Types
- 📌Define Business Goals
- 📌Find the Candidates
- 📌Estimating Total Token Usage
- 📌Gather Vendor Docs and Pricing Pages
- 📌Pipeline Architecture
- 📌Github Repo
- 📌Generating Contents
- 📌Analyzing the articles
- 📌AI as judge
- 📌Pull the result from API
- 📌Finding the Winner
- 📌Introduction
- 📌Live Quality Control
- 📌Build the Live QC
- 📌Wrap up
- 📌Continuous Evaluation