Functions:

• Meet Bard: your creative and helpful collaborator, here to supercharge your imagination, boost your productivity, and bring your ideas to life: Bard is an experiment and may give inaccurate or inappropriate responses. You can help make Bard better by leaving feedback.
• Empowering developers to be more productive and creative with large language models: PaLM API is an easy and safe way to experiment with our large language models. You can access the PaLM API in Vertex AI, Firebase, Colab, and Android today. MakerSuite is a tool we’ve been working on that helps you quickly prototype ideas, reducing AI workflow that used to take days and weeks into minutes. And anyone in your team can use it.
• Introducing Studio Bot, an AI-powered coding assistant that can help Android developers be more productive: Developers can ask questions about Android development, get help fixing code errors, and receive code snippets — all without ever having to leave Android Studio. Studio Bot is in its very early days, and we’re training it to become even better at answering your questions and helping you learn best practices.

Instructions:

• Get your provider's API key: You can usually find the API key in the settings menu of your AI provider.
• Add an AI Connection in Promptitude: In Promptitude, go to your AI Connections and click the New Connection button at the top right.
• In the slide-over, choose your provider and name the connection (just use the provider name for starters). Then paste the API key which you had copied in the previous step.
• Click Save, and you're ready to create your first prompt!

Help Articles:

➔ Understanding Google PaLM: Purpose and Functionality

• Reasoning: PaLM 2 can decompose a complex task into simpler subtasks and is better at understanding nuances of the human language than previous LLMs, like PaLM. For example, PaLM 2 excels at understanding riddles and idioms, which requires understanding ambiguous and figurative meaning of words, rather than the literal meaning.
• Multilingual translation: PaLM 2 was pre-trained on parallel multilingual text and on a much larger corpus of different languages than its predecessor, PaLM. This makes PaLM 2 excel at multilingual tasks.
• Coding: PaLM 2 was pre-trained on a large quantity of webpage, source code and other datasets. This means that it excels at popular programming languages like Python and JavaScript, but is also capable of generating specialized code in languages like Prolog, Fortran, and Verilog. Combining this with its language capabilities can help teams collaborate across languages.