Prompt Engineering for Stable Diffusion
Learn how to craft effective prompts for Stable Diffusion using prompt structuring, weighting, negative prompts, and more to generate high-quality AI images.
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Understanding prompt engineering parameters
Learn how to optimize LLM outputs through strategic parameter settings. This practical guide explains temperature, top-p, max tokens, and other key parameters with real examples to help AI developers get precisely the responses they need for different use cases.
COSTAR Prompt Engineering: What It Is and Why It Matters
Discover how Costar prompt engineering brings structure and efficiency to AI development. Learn this systematic approach to creating better prompts that improve accuracy, reduce hallucinations, and lower costs across different language models.
Mastering role prompting: How to get the best responses from LLMs
Learn how to get better AI responses through role prompting. This guide shows developers how to make LLMs respond from specific expert perspectives with practical examples and best practices.
Delimiters in 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
Lifecycle of a Prompt
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.
What is tree of thought prompting?
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 problems. This