| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1. Welcome to testing LLM applications.mp4 | 64.6 MB | ||
| 2 - LLM Testing - What you need to know now | |||
| 2. Why LLM Testing is a different beast.mp4 | 203.7 MB | ||
| 3 - Environment Setup - Ollama and Python | |||
| 4 - Writing code for Evaluation Metrics | |||
| 10. Measuring Relevance using GEval.mp4 | 43.6 MB | ||
| 11. Measuring Faithfulness using GEval.mp4 | 70.6 MB | ||
| 12. Measuring Hallucination and Citation check using GEval.mp4 | 60.9 MB | ||
| 13. Course Assignment - Module 3_ DeepEval Deep Dive (Metrics).docx | 310.7 KB | ||
| 13. Measuring Toxicity and Bias using GEVal.mp4 | 51.9 MB | ||
| 5 - Testing a mock RAG Application | |||
| 14. Introduction to RAG Application Testing.mp4 | 20.4 MB | ||
| 15. RAG Test run setup.mp4 | 65.1 MB | ||
| 16. Course Assignment - Module 4_ RAG Testing.docx | 310.6 KB | ||
| 16. RAG Testing and Evaluation.mp4 | 127.7 MB | ||
| 16. RAG.ipynb.bin | 177.2 KB | ||
| 16. rag.py | 16.4 KB | ||
| 6 - Agents and their testing | |||
| 17. Challenges in testing AI Agents.mp4 | 32.1 MB | ||
| 18. Agents testing in action.mp4 | 186.6 MB | ||
| 18. Agents.ipynb.bin | 133.1 KB | ||
| 18. Course Assignment - Module 5_ AI Agent & Tool-Call Testing.docx | 311.1 KB | ||
| 18. agents.py | 13.4 KB | ||
| 7 - Promptfoo Introduction and live walkthru | |||
| 19. What is Promptfoo and how it helps.mp4 | 71.1 MB | ||
| 20. Course Assignment - Module 6_ Promptfoo & Adversarial Testing.docx | 516.4 KB | ||
| 20. Promptfoo.ipynb.bin | 161.9 KB | ||
| 20. Testing with Promptfoo - hands on.mp4 | 219.2 MB | ||
| 20. promptfoo.py | 13.7 KB | ||
| 8 - CI CD for LLM Applications and Evaluations | |||
| 21. Important things to note when implementing LLM Evaluations on CI.mp4 | 138.8 MB | ||
| 22. CI_CD.ipynb.bin | 6.2 KB | ||
| 22. Course Assignment - Module 7_ CI_CD Integration.docx | 515 KB | ||
| 22. Important files walkthru for CI.mp4 | 40.2 MB | ||
| 22. ci_cd.py | 3.8 KB | ||
| 9 - Capstone Project Walkthrough - End to End setup with tests,evals and CI | |||
| 23. Building a production ready project with github actions.mp4 | 193.7 MB | ||
| 23. Course Assignment - Module 8_ Capstone Project.docx | 310.4 KB | ||
| llm-testing-capstone-main | |||
| README.md | 4.3 KB | ||
| app | |||
| __init__.py | 102.4 B | ||
| chatbot.py | 7.2 KB | ||
| github | |||
| workflows | |||
| evaluate.yml | 1.1 KB | ||
| gitignore | 307.2 B | ||
| pytest.ini | 307.2 B | ||
| requirements.txt | 0 B | ||
| setup_colab.py | 1.1 KB | ||
| tests | |||
| __init__.py | |||
| conftest.py | 9.9 KB | ||
| test_faithfulness.py | 1.2 KB | ||
| test_relevance.py | 1.3 KB | ||
| test_security.py | 1.1 KB | ||
| knowledge_base.py | 2 KB | ||
| 7. Evaluation_Metrics.ipynb.bin | 356.3 KB | ||
| 7. Google_Colab_Quick_Guide.md | 1.6 KB | ||
| 7. LLM_Evaluation_Metrics_Reference.pdf | 27.8 KB | ||
| 7. Setting up your first evaluation metric using DeepEval.mp4 | 207.3 MB | ||
| 7. evaluation_metrics_complete.py | 38.4 KB | ||
| 8. Setup script and DeepEval Resources.mp4 | 16.8 MB | ||
| 9. Measuring Correctness using GEval.mp4 | 81.7 MB | ||
| 4. Why and what installations will we need.mp4 | 22.4 MB | ||
| 5. Ollama Setup — Commands Reference.docx | 758.3 KB | ||
| 5. Setting up ollama and trying your first attempt at using a model.mp4 | 36.2 MB | ||
| 6. Course Assignment - Module 2_ Environment & Foundations (Ollama).docx | 311 KB | ||
| 6. Python_Pytest_Setup_Guide.docx | 10.2 KB | ||
| 6. Setting up python, and evaluation frameworks.mp4 | 17.3 MB | ||
| 3. Course Homework Assignments - Module 1.docx | 8 KB | ||
| 3. Why LLM Testing Is a Career-Defining Skill for QA at the moment.mp4 | 55 MB |
Testing LLMs : With DeepEval, Promptfoo, RAG & CI/CD
https://WebToolTip.com
Published 9/2026
Created by Madhulika Mitra
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 23 Lectures ( 3h 59m ) | Size: 2 GB
Evaluate RAG apps, red-team for security, and automate quality gates in CI/CD — become job-ready in AI QA
What you'll learn
⚡ Explain why unit, integration, and E2E tests are not enough for generative AI
⚡ Test RAG end to end — retrieval quality and grounded generation along with agentic multi flow testing
⚡ Deepeval evaluation metrics , Promptfoo security testing
⚡ Put evaluations in CI/CD with thresholds, quality gates, and cost control
Requirements
❗ Python programming expertise and Testing fundamentals
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