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Prompt Engineering Guide

😃 Basics

🟢 Введение в ИИ

🟢 Prompting With ChatGPT

🟢 Prompt Engineering

🟢 Learn Prompting Embeds

🟢 Giving Instructions

🟢 Assigning Roles

🟢 Showing Examples

🟢 Combining Techniques

🟢 Formalizing Prompts

🟢 Priming Chatbots

🟢 OpenAI Playground

🟢 LLM Settings

🟢 Pitfalls of LLMs

🟢 Understanding AI Minds

🟢 The Learn Prompting Method

🟢 Starting Your Journey

💼 Applications

🟢 Introduction

🟢 Structuring Data

🟢 Writing An Email

🟢 Study Buddy

🟦 Coding Assistance

🟦 Digital Marketing

🟢 Finding Emojis

🟢 Different Writing Styles

🟢 Summarizing Text

🟢 Zapier for Emails

🟢 Multiple Choice Questions

🟢 Solve Discussion Questions

🟢 Build ChatGPT from GPT-3

🟢 Chatbot + Knowledge Base

🧙‍♂️ Intermediate

🟢 Chain of Thought Prompting

🟢 Zero Shot Chain of Thought

🟦 Self-Consistency

🟦 Generated Knowledge

🟦 Least to Most Prompting

🟦 Dealing With Long Form Content

🟦 Revisiting Roles

🟢 What's in a Prompt?

🤖 Агенты

🟢 Введение

🟦 Использование инструментов LLMs

🟦 LLMs которые рассуждают и действуют

🟦 Код как рассуждение

⚖️ Reliability

🟢 Introduction

🟢 Prompt Debiasing

🟦 Prompt Ensembling

🟦 LLM Self Evaluation

Calibrating LLMs

🖼️ Image Prompting

🟢 Introduction

🟢 Style Modifiers

🟢 Quality Boosters

🟢 Repetition

🟢 Weighted Terms

🟢 Fix Deformed Generations

🟢 Midjourney

🔓 Prompt Hacking

🔨 Tooling

💪 Prompt Tuning

Interpretable Soft Prompts

🎲 Miscellaneous

🟢 Detecting AI Generated Text

🟢 Detection Trickery

🟢 Music Generation

📙 Vocabulary Reference

📚 Bibliography

📦 Prompted Products

🛸 Additional Resources

🔥 Hot Topics

🔨 ToolingPrompt Engineering IDEsLang Chain

Lang Chain

Last updated on August 7, 2024 by Sander Schulhoff

LangChain is a Python library that provides various functionality for building and chaining prompts.

Features

LangChain allows for easy provider switching, standardizes interactions with LLMs, and provides agents for things like symbolic reasoning and MRKL architectures.

LangChain plans to add a graphical user interface in the future.

Footnotes

Karpas, E., Abend, O., Belinkov, Y., Lenz, B., Lieber, O., Ratner, N., Shoham, Y., Bata, H., Levine, Y., Leyton-Brown, K., Muhlgay, D., Rozen, N., Schwartz, E., Shachaf, G., Shalev-Shwartz, S., Shashua, A., & Tenenholtz, M. (2022). ↩

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