Prompt Poet allows you to ground LLM-generated responses to a real-world data context, opening up a new horizon of AI interactions.
Prompt engineering, the discipline of crafting just the right input to a large language model (LLM) to get the desired response, is a critical new skill for the age of AI. It’s helpful for even casual users of conversational AI, but essential for builders of the next generation of AI-powered applications.
Enter Prompt Poet, the brainchild of Character.ai, a conversational LLM startup recently acquired by Google. Prompt Poet simplifies advanced prompt engineering by offering a user-friendly, low-code template system that manages context effectively and seamlessly integrates external data. This allows you to ground LLM-generated responses to a real-world data context, opening up a new horizon of AI interactions.
Prompt Poet shines for its seamless integration of “few-shot learning,” a powerful technique for rapid customization of LLMs without requiring complex and expensive model fine-tuning. This article explores how few-shot learning with Prompt Poet can be leveraged to deliver bespoke AI-driven interactions with ease and efficiency.
Could Prompt Poet be a glimpse into Google’s future approach to prompt engineering across Gemini and other AI products? This exciting potential is worth a closer look.The Power of Few-Shot Learning
In few-shot learning, we give the AI a handful of examples that illustrate the kind of responses we want for different possible prompts. In addition to a few ‘shots’ of how it should behave in similar scenarios.
