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Prompting AI Guide

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Introduction

Basics of prompting

Examples

Introduction

LLM Settings

Prompt elements

Tips

Agents

Introduction to AI Agents

Agent components

Applications

Applications

Context caching with Gemini 1.5 Flash

Fine tuning with GPT 4o models

Function calling with LLMS

Generating code

Generating data

Generating synthetic dataset for RAG

Graduate jou classification case study

Prompt function

Tackling generated datasets diversity

Guides

Prompts intro

Prompts advanced usage

Prompts adversarial

Prompts applications

Prompts basic usage

Prompts ChatGPT

Prompts miscellaneous

Prompts reliability

Prompt hub

Prompt hub

Additional readings

Adversarial prompting

Adversarial prompting in LLMs

Jailbreaking LLMS

Prompt injection in LLMs

Prompt leaking in LLMs

Coding

Drawing TikZ diagram

Generate code snippets with LLMs

LLMs for code generation

Produce MySQL queries using LLMs

Creativity

LLMs for creativity

Interdisciplinary tasks with LLMs

Inventing new words

Proof of infinite primes in shakespeare

Rhyming with proofs

Evaluation

LLM evaluation

Evaluate Plato's dialogue

LLMs for classification

LLMs for classification

Sentiment classification with LLMs

Few shot sentiment classification with LLMs

Image generation

Image generation

Draw a person using alphabet letters

Information extraction

Extract model names from papers

Information extraction with LLMs

LLM research findings

Best practices and lessons learned on synthetic data

Efficient infinite context transformers

How faithful are RAG models

LLM Agents

LLM In-Context recall is prompt dependent

LLM Reasoning

LLM Research findings

LLM Tokenization

LM Guided chain of thought

Reasoning with intermediate revision

Reducing hallucination in structured outputs

Retrieval augmented generation (RAG)

Trustworthiness in LLMs

Mathematics

Mathematical understanding with LLMs

Adding odd numbers with LLMs

Evaluating composite functions

Models

Model prompting guides

ChatGPT prompt engineering

Claude 3

Flan - Scaling instruction finetuned language models

Gemini 1.5 Pro

Gemini Advanced

Gemma

Getting started with Gemini

GPT 4

Grok 1

Llama 3

Llama: open and efficient foundation language models

LLM collection

Mistral 7b LLM

Mistral Large

Mixtral

Mixtral 8x22b

Olmo

Phi 2

Prompting guide for Code Llama

Sora

Question answering

Question answering with LLMs

Closed domain question answering with LLMs

Open domain question answering with LLMs

Science question answering with LLMs

Reasoning

Reasoning with LLMs

Indirect reasoning with LLMs

Physical reasoning with LLMs

Risks & Misuses

Adversarial prompting in LLMs

Biases

Factuality

Risks & Misuses

Text summarizations

Text summarization with LLMs

Explain concepts with LLMs

Truthfulness

Truthfulness in LLMs

Identify hallucination in LLM responses

Prompting Techniques

Prompting techniques

Active Prompt

Automatic prompt engineer (APE)

Automatic reasoning and tool use (ART)

Chain of thought prompting

Directional stimulus prompting

Few shot prompting

Generated knowledge prompting

Graph prompts

Meta prompting

Multimodal COT prompting

PAL (Program aided language models)

Prompt chaining

React prompting

Reflexion

Retrieval augmented generation (RAG)

Self consistency

Tree of thoughts (TOT)

Zero shot prompting

Tools & libraries

Papers

Prompt engineering notebooks

Datasets

Startup Workspace SRL
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Truthfulness in LLMs

This section contains a collection of prompts for exploring truthfulness in LLMs.

Available Topics

Hallucination Identification

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