prompt engineering
Learn how to use delimiters in prompt engineering to improve AI responses. This blog explains delimiter types, best practices, and practical examples for developers working with large language models
prompt engineering
Learn how to master the prompt lifecycle for LLMs - from initial design to production monitoring. A practical guide for AI teams to build, test, and maintain effective prompts using Portkey's comprehensive toolset.
prompt engineering
Large language models (LLMs) keep getting better, and so do the ways we work with them. Tree of thought prompting is a new technique that helps LLMs solve complex problems. It works by breaking down the model's thinking into clear steps, similar to how humans work through difficult
prompt engineering
Explore the key differences between Claude and ChatGPT, from their capabilities and use cases to their response speeds and unique features.
prompting
Prompt security is an emerging and essential field within AI development making sure that AI-generated responses are safe, accurate, and aligned with the intended purpose. When prompts are not secured, the resulting outputs can unintentionally generate or amplify misinformation. Compliance risks are also a major concern. Enterprises deploying AI systems
prompting
Learn how to evaluate prompt effectiveness for AI models. Discover essential metrics and tools that help refine prompts, enhance accuracy, and improve user experience in your AI applications.`
Few-shot prompting
Explore the differences between zero-shot and few-shot prompting to optimize your AI model's performance. Learn when to use each technique for efficiency, accuracy, and cost-effectiveness.
prompt engineering
What is Prompt Engineering? At its core, prompt engineering is about designing, refining, and optimizing the prompts that guide generative AI models. When working with large language models (LLMs), the way a prompt is written can significantly affect the output. Prompt engineering ensures that you create prompts that consistently generate
prompt engineering
Learn how automatic prompt engineering optimizes prompt creation for AI models, saving time and resources. Discover key techniques, tools, and benefits for Gen AI teams in this comprehensive guide.
paper summaries
The paper introduces ReAct, a novel prompt-based paradigm that synergizes reasoning and acting in language models for general task solving. ReAct generates both verbal reasoning traces and actions in an interleaved manner, allowing the model to perform dynamic reasoning to create, maintain, and adj
paper summaries
The paper presents SEEM, a promptable, interactive model for Segmenting Everything Everywhere all at once in an image. It introduces a versatile prompting engine for different types of prompts, including points, boxes, scribbles, masks, texts, and referred regions of another image. The model can ef
paper summaries
The paper introduces AUTOPROMPT, an automated method to create prompts for a diverse set of tasks based on a gradient-guided search. The prompts elicit more accurate factual knowledge from masked language models (MLMs) than manually created prompts on the LAMA benchmark. MLMs can perform sentiment