• Understand the architecture and challenges of RAG pipelines

• Install and configure Ragas in a Python environment

• Evaluate retrieval relevance using built-in metrics

• Measure generation coherence and faithfulness

• Analyze context overlap and hallucination risks

• Use Ragas with popular QA datasets

• Integrate Ragas into LangChain and LLM pipelines

• Visualize evaluation results and score distributions

• Benchmark multiple RAG pipelines side by side

• Apply insights from evaluation to optimize pipeline performance

Course Syllabus

1. Introduction to RAG Pipelines and the Role of Evaluation

2. What is Ragas? Architecture and Use Cases

3. Installing Ragas and Setting Up Your Environment

4. Understanding RAG Evaluation Metrics: Answer, Context, and Faithfulness

5. Using Ragas with Hugging Face and LangChain

6. Loading QA Datasets for Evaluation

7. Evaluating Retrieval: Relevance, Redundancy, and Coverage

8. Evaluating Generation: Coherence, Fluency, and Faithfulness

9. Visualizing Results and Creating Evaluation Reports

10. Using Ragas to Optimize Retrieval-Augmented Systems

11. Case Study: Evaluating an Open-Domain QA System

12. Best Practices for RAG Evaluation in Production Systems

Upon successful completion of this course, learners will receive a Uplatz Certificate of Completion verifying their expertise in RAG system evaluation using Ragas. This certificate reflects your ability to identify bottlenecks in retrieval and generation, apply appropriate metrics, and design reproducible evaluations for production-scale pipelines. Ragas certification adds value to your resume and signals to employers that you can apply transparent evaluation to modern AI systems, making you an asset in any organization deploying LLM-powered applications.

Mastery of RAG pipelines and Ragas evaluation unlocks a unique niche in AI development—focused on trust, transparency, and performance optimization. Organizations using LLMs for customer support, search engines, enterprise knowledge bases, and education platforms need professionals who can not only build but also assess and improve their AI systems.

You’ll be equipped for roles like:

• RAG Evaluation Specialist

• AI QA Engineer

• NLP Research Engineer

• AI System Validator

• Data Scientist (LLM Evaluation Focus)

• AI Product Quality Analyst

These skills are especially valued in industries handling high-stakes content—like legal, healthcare, finance, and academic technology—where hallucinations or incorrect retrieval can have critical consequences. Ragas helps mitigate those risks, and this course gives you the tools to lead responsible AI development.

1. What is Ragas used for?
   Ragas is used to evaluate the performance of Retrieval-Augmented Generation (RAG) pipelines by analyzing retrieval and generation quality.

2. What makes Ragas different from standard LLM evaluation tools?
   Ragas is purpose-built for RAG systems, providing metrics for intermediate steps like retrieval relevance, not just final output.

3. Can Ragas detect hallucinations in generated answers?
   Yes, Ragas evaluates faithfulness and coherence to help detect hallucinated or ungrounded responses.

4. What kinds of metrics does Ragas use?
   It uses metrics such as answer correctness, retrieval relevance, context coverage, and generation faithfulness.

5. How do you integrate Ragas into a pipeline?
   You can use it as a standalone tool or integrate it with frameworks like LangChain for automated evaluation.

6. Is Ragas open-source?
   Yes, Ragas is open-source and can be freely used and extended in custom evaluation setups.

7. What types of data can be evaluated using Ragas?
   QA datasets are the most common, but any RAG-style output (e.g., chatbots, summarizers) can be evaluated.

8. How does Ragas support reproducibility?
   It provides structured evaluation reports, fixed metric implementations, and dataset versioning.

9. What frameworks does Ragas work well with?
   Ragas integrates well with LangChain, Hugging Face Transformers, and OpenAI APIs.

10. Why is RAG evaluation important in production?
    It ensures system responses are grounded in retrieved data and reduces risk of misleading or false answers.