An overview of adversarial prompting in prompt engineering, focusing on understanding risks associated with Large Language Models (LLMs) and strategies for mitigating them.

Adversarial prompting in prompt engineering

Journey of Thought Prompting is an innovative technique that leverages AI to craft more effective prompts for large language models. This approach uses the analytical capabilities of AI assistants to help users create detailed system prompts, fill in missing details, and iteratively refine their prompt engineering skills. It represents a shift towards collaborative AI interaction, enhancing problem-solving capabilities for businesses and engineers.

Journey of thought prompting: Harnessing AI to craft better prompts

An overview of self-refinement, a technique where Large Language Models (LLMs) evaluate and improve their own output without the need for supervised data or reinforcement learning.

LLM's accuracy - self refinement

Essential mental models to improve your LLM interactions and prompt engineering effectiveness

Mental models for prompting

Most prompts fail because they lack structure, but a six-part formula transforms vague requests into precise AI instructions. The difference lies in knowing which components matter most.

Topics > Prompt

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