Introduction

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Last updated on October 1, 2024

In this section, we will explore new prompting techniques to work with diverse Generative AI models. These cutting-edge techniques are designed to refine precision, enhance creativity, and improve problem-solving capabilities of the models. By incorporating these new strategies, you'll be able to get more accurate, relevant, and dynamic responses, helping you make the most out of your AI-driven conversations. Let's explore these fresh approaches and see how they can elevate your prompting game.

Valeriia Kuka

Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.

Introduction

Valeriia Kuka

🟢 Aligned Chain-of-Thought (AlignedCoT)

🟦 Code Prompting

◆ End-to-End DAG-Path (EEDP) Prompting

🔀 For Multimodal Large Language Models (MLLMs)

👀 For Vision-Language Models (VLMs)

◆ Instance-adaptive Zero-Shot Chain-of-Thought Prompting (IAP)

🟦 Logic-of-Thought (LoT)

🟦 Narrative-of-Thought (NoT)

🟦 Self-Harmonized Chain-of-Thought (ECHO)

◆ Skeleton-of-Thought

🟦 Self-Harmonized Chain-of-Thought (ECHO)

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