Sign in Subscribe

prompt engineering

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

Prompt engineering techniques for effective AI outputs

Remember when prompt engineering meant just asking ChatGPT to write your blog posts or answer a basic question? Those days are long gone. We're seeing companies hire dedicated prompt engineers now - it's become a real skill in getting large language models (LLMs) to do exactly what you need them

Prompting Chatgpt vs Claude

Prompting Chatgpt vs Claude

Explore the key differences between Claude and ChatGPT, from their capabilities and use cases to their response speeds and unique features.

Portkey's AI Guardrails

Prompt Security and Guardrails: How to Ensure Safe Outputs

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

Prompt Chaining to break down complex tasks

Using Prompt Chaining for Complex Tasks

Master prompt chaining to break down complex AI tasks into simple steps. Learn how to build reliable workflows that boost speed and cut errors in your language model applications.