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Prompt Engineering Guide

15 / 120·markdown

Introduction

01Introduction
02Basics of Prompting
03Elements of a Prompt
04Examples of Prompts
05LLM Settings
06General Tips for Designing Prompts

Prompting Techniques

07Prompting Techniques
08Active-Prompt
09Automatic Prompt Engineer (APE)
10Automatic Reasoning and Tool-use (ART)
11Self-Consistency
12Chain-of-Thought Prompting
13Directional Stimulus Prompting
14Few-Shot Prompting
15GraphPrompts
16Generated Knowledge Prompting
17Meta Prompting
18Multimodal CoT Prompting
19PAL (Program-Aided Language Models)
20Prompt Chaining
21Retrieval Augmented Generation (RAG)
22ReAct Prompting
23Reflexion
24Tree of Thoughts (ToT)
25Zero-Shot Prompting

AI Agents

26Agents
27AI Workflows vs. AI Agents
28Agent Components
29Context Engineering Deep Dive: Building a Deep Research Agent
30Why Context Engineering?
31Deep Agents
32Function Calling in AI Agents
33Introduction to AI Agents

Guides

34OpenAI 4o Image Generation Guide
35Context Engineering Guide
36OpenAI Deep Research Guide
37Crafting Effective Prompts for LLMs
38Reasoning LLMs Guide

Applications

39LLM Applications & Guides
40Generating Code
41Context Caching with Gemini 1.5 Flash
42Fine-Tuning with GPT-4o Models
43Function Calling with LLMs
44Tackling Generated Datasets Diversity
45Generating Data
46Prompt Function
47Generating Synthetic Dataset for RAG
48Graduate Job Classification Case Study

Prompt Hub

49Prompt Hub
50Adversarial Prompting in LLMs
51Jailbreaking LLMs
52Prompt Injection in LLMs
53Prompt Leaking in LLMs
54LLMs for Classification
55Few-Shot Sentiment Classification with LLMs
56Sentiment Classification with LLMs
57LLMs for Code Generation
58Generate Code Snippets with LLMs
59Produce MySQL Queries using LLMs
60Drawing TiKZ Diagram
61LLMs for Creativity
62Proof of Infinite Primes in Shakespeare Style
63Interdisciplinary Tasks with LLMs
64Inventing New Words
65Rhyming with Proofs
66LLM Evaluation
67Evaluate Plato's Dialogue
68Image Generation
69Draw a Person Using Alphabet Letters
70Information Extraction with LLMs
71Extract Model Names from Papers
72Mathematical Understanding with LLMs
73Evaluating Composite Functions
74Adding Odd Numbers with LLMs
75Question Answering with LLMs
76Closed Domain Question Answering with LLMs
77Open Domain Question Answering with LLMs
78Science Question Answering with LLMs
79Reasoning with LLMs
80Indirect Reasoning with LLMs
81Physical Reasoning with LLMs
82Text Summarization with LLMs
83Explain Concepts with LLMs
84Truthfulness in LLMs
85Identify Hallucination in LLM Responses

Models

86Model Prompting Guides
87ChatGPT Prompt Engineering
88Claude 3
89Prompting Guide for Code Llama
90LLM Collection
91Scaling Instruction-Finetuned Language Models
92Gemini Advanced
93Gemini 1.5 Pro
94Getting Started with Gemini
95Gemma
96GPT-4
97Grok-1
98Kimi K2.5
99Llama 3
100LLaMA: Open and Efficient Foundation Language Models
101Mistral 7B LLM
102Mistral Large
103Mixtral 8x22B
104Mixtral
105OLMo
106Phi-2
107Sora

Risks & Misuses

108Risks & Misuses
109Adversarial Prompting in LLMs
110Biases
111Factuality

Research

112LLM Research Findings
113What is Groq?
114LM-Guided Chain-of-Thought
115Efficient Infinite Context Transformers
116LLM Agents
117LLM Reasoning
118LLM In-Context Recall is Prompt Dependent
119LLM Tokenization
120Reducing Hallucination in Structured Outputs via RAG

Chapter 15

GraphPrompts

GraphPrompts

Liu et al., 2023 introduces GraphPrompt, a new prompting framework for graphs to improve performance on downstream tasks.

More coming soon!

上一章

Few-Shot Prompting

下一章

Generated Knowledge Prompting

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