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prompt engineering

Managing and deploying prompts at scale without breaking your pipeline

Learn how teams are scaling LLM prompt workflows with Portkey, moving from manual, spreadsheet-based processes to versioned, testable, and instantly deployable prompt infrastructure.

Prompting Claude 3.5 vs 3.7

Claude models continue to evolve, and with the release of Claude 3.7 Sonnet, Anthropic has introduced several refinements over Claude 3.5 Sonnet. This comparison evaluates their differences across key aspects like accuracy, reasoning, creativity, and industry-specific applications to help users determine which model best fits their needs. Claude

Basic AI Prompts for Developers: Practical Examples for Everyday Tasks

Ready-to-use prompts that developers can integrate into their daily workflows, tested using Portkey's Prompt Engineering Studio

Accelerating LLMs with Skeleton-of-Thought Prompting

Accelerating LLMs with Skeleton-of-Thought Prompting

A comprehensive guide to Skeleton-of-Thought (SoT), an innovative approach that accelerates LLM generation by up to 2.39× without model modifications. Learn how this parallel processing technique improves both speed and response quality through better content structuring.

Meta prompting: Enhancing LLM Performance

Learn how meta prompting enhances LLM performance by enabling self-referential prompt optimization. Discover its benefits, use cases, challenges, and how Portkey’s Engineering Studio helps streamline prompt creation for better AI outputs

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.

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.