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  • Prompt Engineering Guide
    • Introduction
    • LLM Settings
    • Basics of Prompting
    • Elements of a Prompt
    • General Tips for Designing Prompts
    • Examples of Prompts
    • Prompting Techniques
    • Zero-Shot Prompting
    • Few-Shot Prompting
    • Chain-of-Thought Prompting
    • Self-Consistency
    • Generated Knowledge Prompting
    • Automatic Prompt Engineer (APE)
    • Active-Prompt
    • Directional Stimulus Prompting
    • ReAct Prompting
    • Multimodal CoT Prompting
    • GraphPrompts
    • Prompting Applications
    • PAL (Program-Aided Language Models)
    • Generating Data
    • Graduate Job Classification Case Study
    • Models
    • Scaling Instruction-Finetuned Language Models
    • ChatGPT Prompt Engineering
    • Llama
    • GPT-4
    • Model Collection
    • Risks & Misuses
    • Adversarial Prompting
    • Factuality
    • Biases
    • Papers
    • Tools & Libraries
    • Prompt Engineering Notebooks
    • Datasets
    • Additional Readings
  • Complex ChatGPT Prompts
    • C–Context
    • H–Heritage
    • A–Aim
    • T–Tone
    • GPT
    • Example of ChatGPT-method
  • The Art of ChatGPT Prompting
    • Preface
    • Introduction
    • What is ChatGPT and How Does it Work?
    • The Role of Prompts in ChatGPT Conversations
    • Principles of Clear Communication
    • Constructing Effective Prompts
    • Common Mistakes to Avoid When Crafting ChatGPT Prompts
    • Troubleshooting
    • Technical Issues
    • Case Studies
    • Conclusion
  • Prompt Engineering 101
    • Include direct instructions in prompts
    • Give examples in prompts to get the best response
    • Align prompt instructions with the task’s end goal
    • Use personas to get more specific voices
    • Include acceptable responses in prompts for consistency
    • Try different prompts to find what works best
    • Summary
  • Mastering ChatGPT
    • I. Introduction
    • II. Understanding Prompts
    • III. Tips for Crafting Effective Prompts
    • IV. Advanced Hacks and Techniques
    • V. Avoiding Common Pitfalls
    • VI. Real-life ChatGPT Examples and Case Studies
    • VII. Conclusion
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  1. Prompt Engineering Guide

Model Collection

BERT (opens in a new tab)2018Bidirectional Encoder Representations from TransformersGPT (opens in a new tab)2018Improving Language Understanding by Generative Pre-TrainingRoBERTa (opens in a new tab)2019A Robustly Optimized BERT Pretraining ApproachGPT-2 (opens in a new tab)2019Language Models are Unsupervised Multitask LearnersT5 (opens in a new tab)2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerBART (opens in a new tab)2019Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionALBERT (opens in a new tab)2019A Lite BERT for Self-supervised Learning of Language RepresentationsXLNet (opens in a new tab)2019Generalized Autoregressive Pretraining for Language Understanding and GenerationCTRL (opens in a new tab)2019CTRL: A Conditional Transformer Language Model for Controllable GenerationERNIE (opens in a new tab)2019ERNIE: Enhanced Representation through Knowledge IntegrationGShard (opens in a new tab)2020GShard: Scaling Giant Models with Conditional Computation and Automatic ShardingGPT-3 (opens in a new tab)2020Language Models are Few-Shot LearnersLaMDA (opens in a new tab)2021LaMDA: Language Models for Dialog ApplicationsPanGu-α (opens in a new tab)2021PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel ComputationmT5 (opens in a new tab)2021mT5: A massively multilingual pre-trained text-to-text transformerCPM-2 (opens in a new tab)2021CPM-2: Large-scale Cost-effective Pre-trained Language ModelsT0 (opens in a new tab)2021Multitask Prompted Training Enables Zero-Shot Task GeneralizationHyperCLOVA (opens in a new tab)2021What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained TransformersCodex (opens in a new tab)2021Evaluating Large Language Models Trained on CodeERNIE 3.0 (opens in a new tab)2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationJurassic-1 (opens in a new tab)2021Jurassic-1: Technical Details and EvaluationFLAN (opens in a new tab)2021Finetuned Language Models Are Zero-Shot LearnersMT-NLG (opens in a new tab)2021Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language ModelYuan 1.0 (opens in a new tab)2021Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot LearningWebGPT (opens in a new tab)2021WebGPT: Browser-assisted question-answering with human feedbackGopher (opens in a new tab)2021Scaling Language Models: Methods, Analysis & Insights from Training GopherERNIE 3.0 Titan (opens in a new tab)2021ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationGLaM (opens in a new tab)2021GLaM: Efficient Scaling of Language Models with Mixture-of-ExpertsInstructGPT (opens in a new tab)2022Training language models to follow instructions with human feedbackGPT-NeoX-20B (opens in a new tab)2022GPT-NeoX-20B: An Open-Source Autoregressive Language ModelAlphaCode (opens in a new tab)2022Competition-Level Code Generation with AlphaCodeCodeGen (opens in a new tab)2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program SynthesisChinchilla (opens in a new tab)2022Shows that for a compute budget, the best performances are not achieved by the largest models but by smaller models trained on more data.Tk-Instruct (opens in a new tab)2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP TasksUL2 (opens in a new tab)2022UL2: Unifying Language Learning ParadigmsPaLM (opens in a new tab)2022PaLM: Scaling Language Modeling with PathwaysOPT (opens in a new tab)2022OPT: Open Pre-trained Transformer Language ModelsBLOOM (opens in a new tab)2022BLOOM: A 176B-Parameter Open-Access Multilingual Language ModelGLM-130B (opens in a new tab)2022GLM-130B: An Open Bilingual Pre-trained ModelAlexaTM (opens in a new tab)2022AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelFlan-T5 (opens in a new tab)2022Scaling Instruction-Finetuned Language ModelsSparrow (opens in a new tab)2022Improving alignment of dialogue agents via targeted human judgementsU-PaLM (opens in a new tab)2022Transcending Scaling Laws with 0.1% Extra ComputemT0 (opens in a new tab)2022Crosslingual Generalization through Multitask FinetuningGalactica (opens in a new tab)2022Galactica: A Large Language Model for ScienceOPT-IML (opens in a new tab)2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of GeneralizationLLaMA (opens in a new tab)2023LLaMA: Open and Efficient Foundation Language ModelsGPT-4 (opens in a new tab)2023GPT-4 Technical ReportPanGu-Σ (opens in a new tab)2023PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous ComputingBloombergGPT (opens in a new tab)2023BloombergGPT: A Large Language Model for FinanceCerebras-GPT (opens in a new tab)2023Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster

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