Hello everyone. I'm Balraj Kraj, QA architect and IT specialist expert. Today I'd like to explore how codeless automation and AI prompts empower manual testers. I'm gonna take like around like 20 minutes and I will get discussing about the challenges, the automation barriers, and a new how to democratize automation. The frameworks that we follow to implement those automation, codeless automation and the benefits. And we will also discuss about the common concerns and the takeaways at the end before we see. Okay. Let's get started from manual testing to prompt mastery. How this can be achievable. Yes, this is achievable. This is very beneficiary, I would say, but the first challenging facing we need discuss about the challenges for the manual testers today. There is a widening gap job posting, now demand automation and E screens, creating a shop divided QEA, manual testers. The reach in product knowledge and domain expertise face growing pressure as roles evolve faster than ever. Script based tools like Selenium and Cirus remain out of reach for many non coders. Widening gaps in pay progression and job security. The rising stakes, yes. EA driven development and rapid CACD cycles have raised the bar for quality assurance. Teams are more expert to deliver faster feedback, broader coverage, and continuous integration of test into every bill. The risk. Cease and detest us with invaluable insight into user journeys or being sidelined by technical automation rules, even though their intuition remains irreplaceable. The traditional automation barriers, coding prerequisites, automation rules, demand coding skills like Java, Python, Java, screen. That many experienced manual testers lack with this technical barrier creates an artificial debate between technical and non-technical testers and understanding the complex frameworks. Now, the complex frameworks shift focus from what to test to how to code the test. This distracting from core quality insight and another burden is maintenance script based automation requests constant updates for all UHA. Creating significant technical debt that worries team in maintenance work rather than innovation. A new parum democratizing the test automation. The convergence of codeless automation platforms and prompt engineering techniques offers a transformation solution by combining intuitive to interfaces with well craft DA props. Organizations can empower manual testers to translate their domain expertise into executable test without writing a single line of code. We show simplicity. Codeless platform use drag and wrap interfaces that mirror testers, mental models, making automation accessible and entity from day one. And next one is ary. That unlocks sophisticated test generation, still feeling locators, intelligent assertions through natural language commands. If you are able to handle these two, we can able to preserve the domain expertises test is leverage their replaceable product knowledge while seamlessly adopting modern automation practices on contributing immediately four PACE transformation framework. Platform evolution, knowledge transfer, standardization, the process, and scaling the process In Platform evolution. Initially, we need to access codeless tools based off organization needs, tech stacks compatibility, and a prompting comp capabilities. Look for the platforms that truly democratize the automation. This is very important to take it off and once we have a curated. Platforms that were set Then knowledge transfer train manual testers on visual automation and the problem engineering techniques. Avoiding technical complexity while building confidence through hands-on practices. That way testers will be ready to work on the day one standardization process. Establish the reusable prompt libraries. Test templates and the best practices for consistency. Create a foundation for scalable maintainable automation, then scaling up the process. Integrate the automated test into CACD pipelines, expand coverage systematically and measure impact on release velocity and defect detection rates. Little detail about Pace One evaluating the codeless platforms. There are critical selection criteria that we need to take care of that we rest. All of four three pieces will be in the smooth place. First thing is a native futures. The tools that should support prompt engineering capabilities and intelligent test generation, visual workflow design. The tool that should intuitive builders that mirror testing. Thought processes. Then seamless integration connects to Jira, CACD tools. Test management tools now is all the tools support this, so I think we should be good in this self feeling capabilities adopts automatically to UA changes dramatically reducing maintenance product. That's upcoming even for the code. We were expecting the self fielding capabilities to reduce the maintenance. Then why not? It is with codeless automation, collaboration tools, version control team, libraries, and shared resource. These things we should tools that support all these activities. We need to avoid platforms that merely hide code behind abstractions or record extensive scripting for advanced scenarios. Codeless solution should enable productivity in days, not months. Some of the example I like to explain about the pace one that we discussed about a financial service QA team used. Testing autonomous mode to auto generate one 20 login and a dashboard test from plain English prompts in a week. The key takeaways, A driven test creation saves manuscripting time. And uncovers missed edge cases nearly yearly. Second example, the visual workflows that I'm talking I was talking about earlier. A telecom tester adopted test are recorded to build a regression flows by clicking through the ua. No export editing needed. Key takeaway visual builders mirror a test mental model, allowing manual testers to contribute automation immediately. Phase two, knowledge transfer and upskilling. Reframe the automation concepts. Introduce usual workflows as a natural extension of manual test cases rather than fundamentally different disciplines. Map a familiar testing technology to automation constructs. Introduce prompt engineering basics. Teach testers to write effective prompts that generate accurate locators. Assertions test logic. Start with simple examples before progressing to complex scenarios here, leaving, right? That makes us to feel better on contributing each thing. Hands-on practice provide a sandbox. Environments where test can environment safely without fear of breaking production systems, encourage iterative refinement and peer learning. Now all the software system have their own dedicated test environment. That is the best place to practice whatever they like, because beyond that, there are stage UAT performance platforms. Everything is different. So nowadays, all application have a standard standard test environments to practice with. Celebrate the year livings. Recognize initial automation, success, publicity to build momentum. Showcase how quickly manual testers can contribute meaningful automated coverage. That makes feel better even for other testers. Okay. That's something they feel, that's something that is doable and they're able to move forward with it. It's a special mention about the prompt engineering that is a, the bridge to automation. Prompt engineering bridges the gap to AI powered automation letting use testers use natural language to articulate testing intent. The skill they have already hone from years of writing test cases and bug reports. For example, natural language test. Navigate to checkout, verify total matches, card complete payment, valid card details. It's a pure natural language, but it's a prompt for the com. One trigger or one command intel. How to locate the locate with the natural language. Simple. In a simple word. Find the summit button in the login form. Even if it is ID or class name changes between releases. That way it automatically detects. Submit button, even if it is an ID form or class form. This will take care of it and how to implement the assertions in the command. Confirm error message appears when email format is inval and includes helpful guidance. This is a simple language that gives act as a command for the assess. Navigation effective prompts reduce complexity dramatically allowing testers to focus on what needs testing rather than wrestling with how to coding. This leverages existing domain expertise instead of requiring entirely new technical skills because new technical skill paper lack on the domain knowledge, so that's sometimes it is risky. Some of the examples I like to describe about it reframing automation concepts. A government QA team converted 50 manual UI flows into visual workflows using test and chat GT prompts. Instead of saying, click here, validate there, tested, started saying, verify user registration. Email triggers after successful form submission. This is the natural language Act as a command. These plain language tips. Autogenerated, codeless, automation scripts, the key benefits testers finally saw automation as historically, not the coding. Take prompt writing one example on it. During a two week workshop, test practice transforming acceptance criteria into yay prompts, such as log in with valid credentials. Check the dashboard loads within two seconds. This is the command test. That will evolve it. Tools like BLE and testing converted those into maintainable. Automated test key benefits builds confidence that language is equal to logic. Nothing to think more about it. Once test realized their own words can power automation, confidence skyrockets, and your transformation takes off Phase two, phase three. That is very important. Standardizing and storing all the best practices first. Initially, prompt libraries curate the proven prompts for common testing patterns like login flows. Form validation, data verification, enabling quick reuse and adoption across projects. And also standardize the templates like test templates. Develop standardized frameworks for different tissue types like regression smoke integration, ensuring consistency while maintaining flexibility. Quality guidelines. Establish clear criteria for maintainable automation, naming conventions, documentation standards, and review process that prevent technical data standardization, accelerates onboarding for new team members, improves cross team collaboration, and protects institutional knowledge from being lost to turnover or organizational changes where co-host turns into clarity in the space. Three. Prompt libraries. A state level curating created a centralized to prompt library inside Confluence. It included reusable prompts for our login payment and a data validation across 12 microservices when any prompt was improved. All related issues all to updated via are linked references, key benefits, every tester contributes once and every benefits. Everyone benefits, right? Ones test everywhere That is possible. Tested templates. Some of the example teams in different business units. Build the standard templates for regression smoke and EAP test where tester could drone a base, a PH, check, health check template, customize a few prompts and be up and running in minutes. Key benefits, maintenance. Consistent coverage and structure across products while saving, setup time, pace for scaling and the ca CD integration. Pretty much. Yeah. Scaling of the process is important. Part of the all the. All the four pieces like CACD integration, embed codeless automation into continuous integration pipelines, configure automated test, execution on code, commits full request and scheduled intervals for continuous quality feedback, progressive coverage. Expansion. Start with critical user journeys, then systematically expand to edge cases and integration scenarios. Measure coverage growth alongside defect reduction rates to demonstrate the value performance, monetary. Track key metrics, test, execution, time ness rates, maintenance effort and time to feedback. Use data-driven insights to refine the approaches and demonstrate clear ROI to stakeholders. Some of the example of it. In CACD integration in one of the insurance modernization project, automated pursuits were triggered directly from GitHub actions and assured DevOps pipeline. After every code commit within three sprints, 80 percentage of the regressions checks ran automatically before ing. Key benefits QA become continuous dev defects are caught in hours, not in days for progress coverage expansion. The QA team began with FI critical business flow, like registration, payments, and reports. Over time. They used prompt based test generation to expand coverage to 45 plus edges scenarios. Using dashboards, they dragged automation coverage growth alongside defect reduction threats. Key benefits builds measurable momentum, expanding impact without overloading tips. The transformation becomes self-sustaining. We stop treating QA as a project base. It becomes part of the organization's DNA. Every release, every commit, every prompt adds intelligence to the system. Key takeaways, empowering manual testers, transforms manual testers into automation enablers by executing them with codeless platforms and AI driven tools. Leveraging their invaluable domain expertise. Structured transformation, a four piece framework, guides, organizations through platform evolution, skill development, standardization, and scaling. Faster enable automation success. A prompt for prompt engineering, prompt engineer access the critical bridge, allowing testers to use natural language to define complex test logic and accelerate automation adoption. Enhanced to quality and efficiency. This approach delivers faster release cycles, higher software quality, and employees, QA teams, ensuring they remain central to the development process, transforming QA into a human centered discipline. Human plus EA equal to smart quality. This framework doesn't replace human testers. It imps them by blending codeless automation with AI driven prompts, testers amplify their impact without losing the creative and critical thinking that define real quality. The future of QA isn't just faster, it's more human, more insightful and more collaborative. Measurable benefits of the transformation. These improvements translate directly to accelerate release cycles reduced to production defects, and announced team morale. Quality becomes shared responsibility rather than the bottleneck. Faster test creation codeless approaches grammatically reduce time from test design to execution coverage. Increase the com democratized automation enables broader test coverage across futures and platforms. Maintenance reduction, self-healing test, and yay. Driver updates. Minimize ongoing overhead tester engagement. Manual testers report higher job certification. When with automation skills, safeguarding institutional knowledge. This is the key thing that we were introducing this framework. One of the greatest risk in traditional automation transformations is the loss of domain expertise. When manual testers are sidelined organizations, four feet years of accumulative product knowledge, user behavior, insights, and H case awareness. So we capture the expertise. Testers encode domain knowledge into reusable prompts and visual workflows, and share widely codeless platforms. Make automation assets transparent and accessible to entire teams. Evolve, test, refine, and expand automation based on production insights and emerging scenarios. This virtuous cycle transform institutional knowledge from tribal wisdom into living documentation, re silent to team turnover and scalable across the organization. Some of the common concerns that I'm like to address pretty much everywhere in the social media, but I like to give the answer for that. A replace test entirely. No. A arguments human judgment, but cannot replicate the critical thinking, empathy, and contextual understanding that experienced test is bring. Codeless automation makes test more valuable. Not observ or codeless tools are robust enough for complex scenarios. Modern platforms handle sophisticated workflows including EP testing, database validation, and conditional logic prompt. Prompt engineering unlocks advanced capabilities without coding requirements. What about vendor locking? Choose platforms with export capabilities and open integrations. Prioritize tool that announced rather than replace existing process, allowing gradual adoption and flexibility. Key takeaways, democratize automation with Codeless platform and follow the four PACE framework. Leverage the prompt engineering as a bridge skill, then amplify the institutional knowledge. Thank you everyone for listening this, I hope this was very helpful for all the organizations that who are undergoing all the EA upgrades. Thank you.