Learn Prompting

Prompt Engineering Guide

😃 Basics

🟢 Basics Guide Overview

🟢 What is Generative AI?

🟢 ChatGPT Basics

🟢 Testing Prompts with Interactive Learn Prompting Embeds

🟢 Introduction to Prompt Engineering

🟢 Basic Prompt Structure and Key Parts

🟢 Technique #1: Instructions in Prompts

🟢 Technique #2: Roles in Prompts

🟢 Technique #3: Examples in Prompts: From Zero-Shot to Few-Shot

🟢 Combining Prompting Techniques

🟢 Tips for Writing Better Prompts

🟢 Prompt Priming: Setting Context for AI

🟢 Differences Between Chatbots and LLMs

🟢 LLM Limitations: When Models and Chatbots Make Mistakes

🟢 What Can Generative AI Create Beyond Text?

🟢 How to Solve Problems Using Generative AI: A Simple Method

🟢 Next Steps: Where to Go From Here

💼 Applications

🟢 Introduction

🟢 Text Summarization

🟢 Table Generation

🟢 Multiple Choice Questions

🟢 Short-Form Content

🟢 Writing in Different Styles

🟢 Finding Emojis

🟢 Writing Emails

🟢 Blog Writing

🟢 Legal Documents

🟢 Study Buddy

🟦 Digital Marketing

🟦 Coding Assistance

🟦 Knowledge Base Chatbot

🟦 How to Build a Chatbot Using LLMs

🟦 Zapier for Emails

🧙‍♂️ Intermediate

🟢 Introduction

🟢 Chain-of-Thought Prompting

🟢 Zero-Shot Chain-of-Thought

🟦 Self-Consistency

🟦 Generated Knowledge

🟦 Least-to-Most Prompting

🟦 Dealing With Long Form Content

🟦 Revisiting Roles

🟦 More About Prompt Elements

🟦 Basic LLM Settings

🟦 OpenAI Playground

🧠 Advanced

🟢 Introduction

Zero-Shot

🟢 Introduction

🟢 Emotion Prompting

🟢 Role Prompting

🟢 Re-reading (RE2)

🟢 Rephrase and Respond (RaR)

◆ System 2 Attention (S2A)

Few-Shot

🟢 Introduction

🟢 Self Generated In-Context Learning (SG-ICL)

🟢 Chain-of-Dictionary (CoD)

🟦 Chain of Knowledge (CoK)

◆ K-Nearest Neighbor (KNN)

◆◆ Prompt Mining

Thought Generation

🟢 Introduction

🟢 Chain of Draft (CoD)

🟦 Contrastive Chain-of-Thought

🟦 Automatic Chain of Thought (Auto-CoT)

🟦 Tabular Chain-of-Thought (Tab-CoT)

🟦 Memory-of-Thought (MoT)

🟦 Active Prompting

🟦 Analogical Prompting

🟦 Complexity-Based Prompting

🟦 Step-Back Prompting

🟦 Thread of Thought (ThoT)

Ensembling

🟢 Introduction

🟢 Universal Self-Consistency

🟦 Mixture of Reasoning Experts (MoRE)

🟦 Max Mutual Information (MMI) Method

🟦 Prompt Paraphrasing

🟦 DiVeRSe (Diverse Verifier on Reasoning Step)

🟦 Universal Self-Adaptive Prompting (USP)

🟦 Consistency-based Self-adaptive Prompting (COSP)

🟦 Multi-Chain Reasoning (MCR)

Self-Criticism

🟢 Introduction

🟢 Self-Calibration

🟢 Chain of Density (CoD)

🟢 Chain-of-Verification (CoVe)

🟦 Self-Refine

🟦 Cumulative Reasoning

🟦 Reversing Chain-of-Thought (RCoT)

◆ Self-Verification

Decomposition

🟢 Introduction

🟢 Chain-of-Logic

🟦 Decomposed Prompting

🟦 Plan-and-Solve Prompting

🟦 Program of Thoughts

🟦 Tree of Thoughts

🟦 Chain of Code (CoC)

🟦 Duty-Distinct Chain-of-Thought (DDCoT)

◆ Faithful Chain-of-Thought

◆ Recursion of Thought

◆ Skeleton-of-Thought

⚖️ Reliability

🟢 Introduction

🟢 Prompt Debiasing

🟦 Prompt Ensembling

🟦 LLM Self-Evaluation

🟦 Calibrating LLMs

🔓 Prompt Hacking

🖼️ Image Prompting

🟢 Introduction

🟢 Style Modifiers

🟢 Quality Boosters

🟢 Repetition

🟢 Weighted Terms

🟢 Fix Deformed Generations

🟢 Midjourney

🌱 New Techniques

🟢 Introduction

🟢 Aligned Chain-of-Thought (AlignedCoT)

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

🟦 Logic-of-Thought (LoT)

🟦 Narrative-of-Thought (NoT)

🟦 Code Prompting

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

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

🔧 Models

🟢 Introduction

🟢 Stable Diffusion 3.5

🟢 Anthropic Claude

🟦 Apple Intelligence Models

🟢 Google Gemini 1.5

🟢 Gemini 1.5 Flash

🟢 Gemini 1.5 Pro

🗂️ RAG

🟢 Introduction

🟦 Retrieval-Augmented Generation (RAG)

🟦 FLARE / Active RAG

🟦 Corrective RAG

🟦 Speculative RAG

🟦 Reliability-Aware RAG (RA-RAG)

🟦 Multi-Fusion Retrieval Augmented Generation (MoRAG)

🤖 Agents

🟢 Introduction

🟦 LLMs Using Tools

🟦 LLMs that Reason and Act

🟦 Code as Reasoning

💪 Prompt Tuning

🟢 Introduction

🟦 Prompt Tuning with Soft Prompts

🟦 Interpretable Soft Prompts

🟦 Prefix-Tuning

🟦 Prompt-Tuning with Perturbation-Based Regularizer

🟦 Low-Rank Prompt Tuning (LoPT)

🟦 Dynamic Prompting

🟦 Gradient-Free Prompt Tuning

🟦 Multitask Prompt Tuning

🔁 Language Model Inversion

🟢 Introduction

🟢 logit2prompt

🟢 output2prompt

🟢 Reverse Prompt Engineering (RPE)

🔨 Tooling

📙 Vocabulary Resource

🎲 Miscellaneous

🟢 Introduction

🟢 Detection Trickery

🟢 Music Generation

🟢 Detecting AI Generated Text

📚 Bibliography

📦 Prompted Products

🛸 Additional Resources

🔥 Hot Topics

📙 Vocabulary Resource

Prompt Engineering Technique

Reading Time: 1 minute

Last updated on November 12, 2024

A prompt engineering technique is a strategy for iterating on a prompt to improve it. In production or academic settings, this is often automated, but in consumer settings, it is often manually.

Footnotes

Schulhoff, S., Ilie, M., Balepur, N., Kahadze, K., Liu, A., Si, C., Li, Y., Gupta, A., Han, H., Schulhoff, S., & others. (2024). The Prompt Report: A Systematic Survey of Prompting Techniques. arXiv Preprint arXiv:2406.06608. ↩

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.