2024’s EASY GUIDE to PROMPT Engineering ai

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AI Summary

Summary: Prompt Engineering in 2024

• Prompt Engineering Basics
  • Utilize API options: Chat Completion API and Completion API.
  • System messages guide AI interactions.
  • System messages can specify context, style, and restrictions.
  • Short learning examples help AI understand tasks.
  • Define output structure in system messages for clarity.
• Advanced Prompt Engineering Techniques
  • Break down complex tasks into simpler steps.
  • Use affordances like search queries for verification.
  • chain-of-thought-prompting for step-by-step reasoning.
  • Specify output structure for citations or structured formats.
  • Adjust model parameters for creative or factual responses.
• Prompt Engineering for Specific Scenarios
  • Grounding context to ensure accuracy and relevance.
  • Use clear instructions and structured prompts.
  • Experiment with different prompt styles.
  • Understand model capabilities and limitations.
  • Use template languages like Liquid for structured input.
• Effective Prompting Strategies
  • Be specific and direct in prompts.
  • Provide context and examples.
  • Break down complex tasks.
  • Use proper formatting.
  • Understand model limitations.
  • Use natural language and avoid jargon.
  • Test and refine prompts.
  • Use positive reinforcement.
  • Leverage model strengths.
  • Consider ethical implications.
• OpenAI’s Prompt Engineering Guide
  • Write clear instructions.
  • Provide reference text.
  • Split complex tasks into subtasks.
  • Give the model time to synthesize.
  • Use external tools.
  • Test changes systematically.
• Prompt Engineering for Education
  • Compare student solutions to correct answers.
  • Provide hints for incorrect answers.
  • Evaluate student reasoning, not just the final answer.
• Prompt Engineering for Customer Service
  • Classify queries into categories.
  • Provide troubleshooting steps.
  • Connect users to appropriate departments.
• Prompt Engineering for Document Analysis
  • Use triple quotes for document excerpts.
  • Ensure context is included for accurate analysis.
• Prompt Engineering for AI Development
  • Google’s Jini Ultra aims to outperform GPT-4.
  • Benchmarking and performance comparison between models.
  • Techniques to improve GPT-4’s performance without retraining.

The above summary outlines the key points from a comprehensive discussion on prompt engineering as it stands in 2024, including basic and advanced techniques, strategies for specific scenarios, educational applications, customer service automation, document analysis, and the ongoing development and competition between AI models like GPT-4 and Google’s Jini Ultra.