Sign in Subscribe

prompt engineering

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.

Prompt engineering vs. fine-tuning: What’s better for your use case?

Discover the key differences between prompt engineering and model fine-tuning. Learn when to use each approach, how to measure effectiveness and the best tools for optimizing LLM performance.

Prompt engineering for low-resource languages

Dive into innovative prompt engineering strategies for multilingual NLP to improve language tasks across low-resource languages, making AI more accessible worldwide

What is tree of thought prompting?

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