[100% Off] Complete Rag Testing Course With Ragas Deepeval And Python
Learn the complete way to test RAG implementations. From functional to performance from Python to RAGAs and DeepEval
What you’ll learn
- Understand the Basics of LLMs
- Understand LLM Application types
- Gain know how on types of AI – Weak and Generative
- Understand How RAG works
- Understand the types of RAG Testing
- A lot of ready to use code that can be used from moment 0
- Understand ML metrics such as Accuracy, Recall and F1
- Understand RAG Testing Metrics such as Context Recall, Context Accuracy
- Understand RAG Testing Metrics such as Answer Relevancy
- Understand RAG Testing Metrics such as Truthfulness
- Gain know how with RAGAs open source Testing framework
- Gain know how with DeepEval open source Testing framework
- Understand how to create custom metrics
- Test for Coherence, Fluency, tone and other human specific metrics
- Rapid validation tools for MVPs using RAG systems.
- Deep understanding of metrics (fluency, coherence, relevance, conciseness), customizable test frameworks.
Requirements
- Some basic Python programing experience
- Basic understanding of LLMs and AI
- A LLM API Key
- Basic Testing understanding
- Laptop/ PC with VS Code
- Willingness to learn a new hot skill
Description
Master the art of evaluating Retrieval-Augmented Generation (RAG) systems with the most practical and complete course on the market — trusted by over 25,000 students and backed by 1,000+ 5-star reviews.
Whether you’re building LLM applications, leading AI QA efforts, or shipping reliable MVPs, this course gives you all the tools, code, and frameworks to test and validate RAG pipelines using DeepEval and RAGAS.
What You’ll Learn
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Understand the Basics of LLMs and how they are applied across industries
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Explore different LLM Application Types and use cases
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Learn the difference between Weak AI and Generative AI
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Deep-dive into how RAG works, and where testing fits into the pipeline
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Discover the types of RAG Testing: factuality, hallucination detection, context evaluation, etc.
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Get hands-on with ready-to-use code from Day 0 — minimal setup required
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Master classic ML metrics (Accuracy, Recall, F1) and where they still matter
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Learn RAG-specific metrics:
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Context Recall
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Context Accuracy
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Answer Relevancy
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Truthfulness
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Fluency, Coherence, Tone, Conciseness
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Build custom test cases and metrics with DeepEval and RAGAS
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Learn how to use RAGAS and DeepEval open-source frameworks for production and research
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Validate MVPs quickly and reliably using automated test coverage
Who is This For?
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AI & LLM Developers who want to ship trustworthy RAG systems
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QA Engineers transitioning into AI testing roles
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ML Researchers aiming for reproducible benchmarks
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Product Managers who want to measure quality in RAG outputs
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MLOps/DevOps professionals looking to automate evaluation in CI/CD
Author(s): Dan Andrei Bucureanu
