Companies want to train and use large language models (LLMs) with their own proprietary data. Open source generative models such as Meta’s Llama 2 are pivotal in making that possible. The next hurdle is finding a platform to harness the power of LLMs. Snowflake lets you apply near-magical generative AI transformations to your data all in Python, with the protection of its out-of-the-box governance and security features.
We’ve got something truly special in store for you. We reached out to our expansive testing community, consisting of 40,000 testers, and posed a question about leveraging GPT prompts for various software testing scenarios and tips for effective prompting. The response was nothing short of astounding, and today, we’re thrilled to bring you the incredible insights we gathered. Prepare to be amazed as we unveil 15+ best ChatGPT prompts for software testing enthusiasts like you.
There is no doubt about it: Artificial Intelligence (AI) and Machine Learning (ML) has changed the way we think about software testing. Ever since the introduction of the disruptive AI-powered language model ChatGPT, a wide range of AI-augmented technologies have also emerged, and the benefits they brought surely can’t be ignored. In this article, we will guide you to leverage AI/ML in software testing to bring your QA game to the next level.
Today, we are excited to announce the support of Dockerfile based deployments in general availability. You can now deploy any GitHub repository that contains a Dockerfile across all our locations worldwide. It can be used to deploy APIs, full-stack applications as well as workers with no extra cost. Building and deploying using Dockerfiles offers more flexibility: you can deploy any kind of application, framework, and runtime, including with custom system dependencies.
