TLDR;
This YouTube video provides a comprehensive guide on how to use AI tools, specifically focusing on prompt engineering for software testing and quality assurance. It covers various prompting techniques, frameworks, and practical examples to enhance productivity and accuracy in manual and automation testing. The session also touches on resume building and job application strategies using AI.
- Introduction to Prompt Engineering
- Types of Prompting and Frameworks
- Practical Applications in QA
- Resume Building and Job Application Strategies
Introduction: AI's Impact on Job Security and the Importance of Prompting [0:00]
The video starts by polling the audience on whether AI will replace their jobs within five years, revealing mixed reactions. It highlights the importance of learning AI to avoid being replaced by someone who knows AI. The session aims to teach viewers how to use AI tools like chat GPT effectively in their daily lives as manual testers, automation engineers, and API testers. The presenter shares his 1.5 years of experience using AI and promises practical examples.
Agenda: Learning Objectives and Practical Applications of AI in QA [2:20]
The agenda includes understanding prompt engineering, different types of prompting, and various prompting frameworks. The session will cover how to use chat GPT for requirement analysis, test plan creation, test case generation, bug detection, and test closure techniques. It also includes effective coding techniques, technical interview preparation, and resume building. The presenter emphasizes a practical, hands-on approach rather than theoretical concepts.
Audience Interaction and Initial Poll Results [8:01]
The presenter shares a form asking whether AI will replace jobs in the next five years. The results show a mixed reaction, with most participants feeling confident that AI will not replace them. The presenter emphasizes the importance of learning how to use AI in daily tasks, particularly in QA roles.
AI Tools and the QA Perspective [10:21]
The session focuses on how to use AI in the QA perspective, utilizing tools like chat GPT 4.0, Gemini, and cloud. a. The presenter recommends using multiple large language models (LLMs) to find the best results. He introduces global jet GPT, which allows users to test multiple LLMs. The goal is to 2x productivity as a tester by learning how to use these tools effectively.
Understanding Prompting: Definition and Types [12:52]
Prompting is defined as giving instructions to a machine or AI tool. The presenter explains that prompting is similar to giving instructions to a person, using the example of a mother asking her son to buy milk. The session focuses on QA-specific prompting, excluding visual, video, and question-based prompting.
Prompting Principles: Simple, Refine, Iterate [15:47]
The three principles of creating a prompt are to start simple, refine, and iterate to improve. The presenter criticizes the use of one-liner prompts, calling them a "super bad technique." He introduces the concept of "zero short prompting," which lacks context and examples, and explains that it is only suitable for simple questions like fixing spelling mistakes.
Precision Prompting: Adding Context and Examples [20:29]
The presenter introduces "few short prompting" and "precision prompts," which involve giving more context to the AI. He uses the example of asking a brother to buy a specific chocolate to illustrate the importance of providing detailed instructions. Direct prompts do not work well; proper context is necessary for effective communication with LLMs.
Types of Prompts: Direct, Contextual, and Role-Based [22:19]
The session covers three types of prompts: direct, contextual, and role-based. Direct prompts are the worst way to connect with AI. Contextual prompts involve providing background information and examples. Role-based prompts assign a specific role to the AI, such as acting as a 20-year experienced software tester. The focus will be on contextual and step-by-step prompts.
Resources for Learning Prompting [25:31]
The best website to learn prompting is learn prompting toor, which offers a beginner-free course called "chat GPT for everyone." The presenter advises viewers to take this course after the workshop.
Prompting Frameworks: SWAT Analysis [27:27]
The presenter introduces prompting frameworks, emphasizing that they are best practices and not mandatory. The first framework discussed is SWAT (Strengths, Weaknesses, Opportunities, Threats). He demonstrates how to use the SWAT framework to analyze someone aiming to advance their career in software testing and automation. By providing the AI with strengths, weaknesses, opportunities, and threats, the response is more accurate and relevant.
Comparing LLMs: chat GPT vs. Cloud 3 Opus [35:57]
The presenter compares the responses from chat GPT 4.0 and cloud. a using the SWAT framework. Cloud 3 Opus provides a much better and more accurate plan compared to chat GPT 4.0. He emphasizes the importance of testing with multiple LLMs to find the best one.
Prompting Frameworks: STAR Method [37:07]
The second framework discussed is STAR (Situation, Task, Action, Result). The presenter explains how to provide the AI with the situation, task, action, and expected results to get a more accurate response.
How Not to Use chat GPT: Avoiding Zero Short Prompts and Hallucinations [38:35]
The session covers how not to use chat GPT, emphasizing that zero short prompts are rarely useful. The presenter advises giving more examples, teaching the AI, and using two or more prompts to get the best answer. If the AI starts hallucinating (giving random answers), restart the conversation. He also warns against using personal or confidential information and using chat GPT for illegal activities.
Practical Examples: Role-Based Prompting and Contextualization [42:14]
The presenter demonstrates how to use role-based prompting by telling the AI to act as a quality assurance engineer with 20 years of experience. He then asks for advice on testing an e-commerce website like amazon.com. To make the AI more aware, he gives more context by providing an image of the website and asking specific questions about the registration module. This approach yields more accurate and relevant advice.
Manual Testing: Requirement Analysis and Test Plan Creation [52:09]
The presenter demonstrates practical manual testing by performing requirement analysis using AI. He uploads a PDF of the requirements for an AB testing tool and asks the AI to analyze the requirements and suggest what to include in the test plan and test strategy. He refines the test plan by giving a proper template and asking the AI to fill in the details.
Test Cases and Bug Reporting [1:01:11]
The presenter explains how to generate test cases in a proper JIRA format and includes negative scenarios. He emphasizes the importance of giving the AI a template to follow. He also demonstrates how to use AI for bug detection and reporting, providing a context-rich example of a user not being able to log in after three attempts.
Test Metrics and Test Closure [1:06:47]
The presenter shows how to use AI to create test metrics and test closure reports. He provides prompts that generate test metrics templates and test closure reports automatically.
Key Learnings and Announcements [1:10:27]
The key learnings include using context-based and role-based prompts, giving more context, and refining prompts. The presenter announces a Python batch starting on May 31st, covering Python API automation and Selenium.
Python Batch Details and Testimonials [1:16:10]
The presenter shares testimonials from previous Python batch students and provides details about the course, including timings, content, and enrollment information.
Testing Multiple LLMs and Cold Email Series [1:20:10]
The presenter emphasizes the importance of testing with multiple LLMs to find the best response. He demonstrates how to create a cold email series for job applications using AI. By uploading a resume and job description, the AI generates a series of personalized emails to send to HR.
Cold Email Series Technique [1:23:29]
The presenter explains the cold email series technique, where AI analyzes a resume and job description to create personalized follow-up emails. This technique helps job seekers stand out and increase their chances of getting a response.
Global chat GPT and Co-pilot [1:31:45]
The presenter discusses global chat GPT, which allows users to access multiple LLMs for a small fee. He also explains that co-pilot is primarily for coding but can also be used for creating test cases.
Session Recap and Future Topics [1:34:22]
The presenter recaps the session and announces that there will be another session covering advanced problem statements, performance testing, resume building, coding effectively, bug finding, API testing, and web testing.
