Image credit: nature In AI, learning isn’t one-size-fits-all. Models learn from data in different ways, and understanding the three core types – supervised, unsupervised and deep learning (descriptive learning) – can help us better grasp how modern AI powers everyday applications, especially recommendation systems on platforms like Netflix and Amazon. Supervised learning is like training […]
Image credit: statsig Fine tuning the behavior of an AI model is not just about writing the right prompt-It is also about adjusting the temperature behind the scenes. In LLMs temperature controls how creative or focused the model’s output will be. A lower temperature such as 0.0-0.2 keeps the response consistent, fact based and deterministic, […]
Image Credit: Encord Fine tuning parameters in AI models is more than adjusting dials- it’s about aligning the model’s behavior with real world need. When dealing large and dynamic datasets, especially in industries like retails, the right parameter tuning ensures that outputs are not accurate but context aware. Instead of relying on default settings, adjusting […]
I personally used the Groq API to test and compare how two AI models perform with the same input. I compared two powerful models—LLaMA 3 70B Versatile and DeepSeek R1 Distill LLaMA 70B—by sending an identical JSON prompt requesting Selenium Java code for Salesforce login automation. My goal was to analyze and test how these models handle […]
Image Credits: Katalon In Software Testing, a solid test strategy is more than just a document. It’s a blueprint for quality and it defines what needs to be tested, how, when, by who, and which tools. In traditional crafting a test strategy involved manual effort, relying heavily on experience, risky guesswork and time consuming planning […]
Software testing has been a key part of high quality product delivery, but most traditional methods require a significant manual work and technical knowledge. Gen AI Testing is transforming this, by enabling testers to interact with AI via basic prompts without needing deep automation expertise. Instead of building complex test scripts, testers can ask AI […]
As technology evolves rapidly, delivering flawless software has become a top priority. QA teams often deal with repetitive tasks like running tests, reporting bugs, and checking for UI issues across different browsers. AI-powered personal bot browser extensions can change the way QA teams work by automating these tasks, reducing human effort, and making testing more […]
AI- powered test case generation is transforming software testing by automating one of the most time consuming and critical tasks in quality assurance. Traditional test case creation relies heavily on manual effort, requiring testers to analyze requirements, design test scenarios, and ensure full coverage. This approach often results in inefficiencies, missed edge cases, and increased […]
In today’s software development world, the pressure to deliver high-quality software applications quickly is unrelenting. Continuous testing has emerged as a vital practice, ensuring that testing is integrated at every stage of the development lifecycle. However, traditional approaches often struggle to keep up with the speed of Agile and DevOps pipelines, leaving gaps in quality […]
Agile Methodologies revolutionized the way software is developed, moving away from rigid, waterfall-style approaches to iterative, incremental delivery. However, when Agile frameworks like SAFe (Scaled Agile Frameworks) and LeSS (Large-Scale Scrum) come into play, the complexity of quality assurance escalates significantly. Testing in scaled Agile Environments demands not only technical adaptability but also a cultural […]
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