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

Few-Shot Learning

Reading Time: 1 minute

Last updated on November 12, 2024

Few-Shot learning is a machine learning paradigm that aims to increase the accuracy of a model by training on a small number of examples¹². This is not to be confused with Few-Shot Prompting, which more specifically refers to the prompting technique.

Footnotes

Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 594–611. ↩
Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (Csur), 53(3), 1–34. ↩

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.

Prompt Engineering Guide

Master Generative AI with Our Courses

Need Business GenAI Training?

Contact Sales

Want to keep learning

Explore Our Full Course Collection

© 2025 Learn Prompting. All rights reserved.

Privacy Policy Terms of Service