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  002. Chapter 1. Enhancing testing with large language models.mp4

  004. Chapter 1. Summary.mp4

  006. Chapter 2. Avoiding the risks of using LLMs.mp4

  008. Chapter 2. Examining the principles of prompt engineering.mp4

  010. Chapter 2. Creating a library of prompts.mp4

  012. Chapter 2. Summary.mp4

  014. Chapter 3. How tools help with testing.mp4

  016. Chapter 3. Summary.mp4

  018. Chapter 4. AI-assisted testing for developers.mp4

  020. Chapter 4. Building in quality with AI assistance.mp4

  022. Chapter 4. Improving documentation and communication with LLMs.mp4

  024. Chapter 4. Summary.mp4

  026. Chapter 5. Focused prompts with the use of models.mp4

  028. Chapter 5. LLMs and test cases.mp4

  030. Chapter 6. Rapid data creation using AI.mp4

  032. Chapter 6. Setting up LLMs as test data managers.mp4

  034. Chapter 6. Summary.mp4

  036. Chapter 7. Improving existing UI automation.mp4

  038. Chapter 8. Assisting exploratory testing with artificial intelligence.mp4

  040. Chapter 8. Summarizing testing notes with LLMs.mp4

  042. Chapter 9. AI agents as testing assistants.mp4

  044. Chapter 9. Moving forward with AI test assistants.mp4

  046. Part 3. Context Customizing LLMs for testing contexts.mp4

  048. Chapter 10. Embedding context further into prompts and LLMs.mp4

  050. Chapter 11. Contextualizing prompts with retrieval-augmented generation.mp4

  052. Chapter 11. Enhancing data storage for RAG.mp4

  054. Chapter 12. Fine-tuning LLMs with business domain knowledge.mp4

  056. Chapter 12. Summary.mp4

  058. Appendix B. Setting up and using GitHub Copilot.mp4

  060. Appendix C. Exploratory testing notes.mp4