RAG Evaluation Course

The RAG Evaluation Course is designed to help developers and AI engineers move beyond building AI systems to understanding whether those systems actually work.

Most teams build Retrieval-Augmented Generation (RAG) pipelines that appear to produce correct answers — but in reality, these systems often fail silently due to:

• Poor retrieval quality

• Incorrect context assembly

• Hallucinated or unsupported responses

Without proper evaluation, these issues go unnoticed until they impact real users.

This course provides a structured and repeatable framework to evaluate RAG systems at every stage.

You will begin by understanding why traditional machine learning metrics fail in RAG systems and how evaluation must be broken down into three stages:

1. Retrieval

2. Context Assembly

3. Generation

You will then build a golden dataset, which serves as the foundation for evaluation. This includes:

• Queries

• Expected answers

• Ground-truth source documents

Next, you will learn how to measure:

Retrieval Quality

Using metrics like:

• Recall@K

• Precision@K

• Mean Reciprocal Rank (MRR)

• NDCG

Generation Quality

Evaluating:

• Faithfulness

• Completeness

• Hallucination detection

• Answer-to-context alignment

Finally, you will automate the entire evaluation pipeline using an LLM-as-judge approach, where AI models score outputs using structured evaluation prompts.

By the end of this course, you will build a complete RAG evaluation system that:

• Identifies failures accurately

• Attributes errors to the correct stage

• Provides actionable improvement insights

This course transforms you from someone who builds AI systems to someone who can validate and optimize them for production. 

What You Will Learn

• Why RAG evaluation is fundamentally different from traditional ML evaluation

• The three major failure points in RAG pipelines

• How to create and use golden datasets for evaluation

• Measuring retrieval performance using ranking metrics

• Evaluating generation quality and detecting hallucinations

• Performing end-to-end evaluation and error attribution

• Automating evaluation using LLM-as-judge frameworks

• Designing evaluation pipelines for production systems

Tools & Technologies

• Python

• Jupyter Notebook / Google Colab

• RAG Pipelines

• Evaluation Metrics Frameworks

• LLM APIs (for automated evaluation)

• Pydantic (for structured outputs)

Who Should Enroll

• Developers building RAG-based applications

• AI engineers working on LLM systems

• Freelancers creating AI chatbots or assistants

• Startup founders launching AI products

• Engineers struggling with unreliable AI outputs

• Anyone who wants to improve AI system accuracy

Real-World Use Cases

• Evaluating AI chatbots and assistants

• Improving enterprise knowledge systems

• Debugging RAG-based SaaS products

• Measuring AI response accuracy

• Building AI quality assurance pipelines

• Production AI system validation

Why This Course Matters

Most developers build AI systems…

This course gives you the missing layer of AI engineering: evaluation

Enroll Now

Stop guessing if your AI system works.

Start building measurable, reliable, and production-ready RAG systems.

Limited seats available — enroll today.