Prompt Engineering Master Class for ENGINEERS with Ollama and LLM (Q4 2024 Update)

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AI Summary

Summary of Video Transcript

• Prompt Engineering Importance: The video emphasizes the significance of prompt engineering in the evolving landscape of AI and language models, particularly with the advent of local models like Quinn 2.5. It suggests that mastering prompt engineering is crucial for success in 2025 and beyond.

• Four-Level Framework for Prompts:

  • Level 1: Basic prompts that can be run in the terminal using CLI tools like llm and olama. These prompts can be tweaked for different outputs.
  • Level 2: Reusable prompts with static variables and structured formats (like XML) that improve performance. These prompts are more defined and can be used to solve well-defined problems.
  • Level 3: Prompts that include examples to guide the output, making them more precise and tailored for specific outputs.
  • Level 4: Dynamic prompts that are scalable and can be integrated into code for applications, allowing for automatic updating of variables.

• Practical Examples:

  • Demonstrates creating and running prompts in the terminal, switching between different models, and adjusting prompts for various outputs.
  • Shows how to create a reusable prompt for converting a TypeScript interface into a SQL table.
  • Illustrates the creation of a prompt template for summarizing content with specific instructions and examples.

• Prompt Libraries and Tools:

  • Discusses the importance of having a personal prompt library and the ability to rapidly prototype and understand model outputs.
  • Introduces a tool for managing prompt libraries and executing prompts with dynamic variables.

• Upcoming Content:

  • The video teases future content on meta prompting, 2025 predictions, AI coding benchmarks, and a foundational AI coding course.

Detailed Instructions and URLs

• CLI Commands:
  • Create an empty file: touch prompt.txt
  • Run prompts on a file: llm <prompt_file>
  • List available models: llm models
  • Run a prompt with a specific model: llm -m <model_name> <prompt_file>
  • Use olama to run prompts: olama run <model_name> <prompt_file>
  • Create a script to run multiple models: touch many_models.sh
• URLs: No URLs were provided in the transcript.

Tips

• Structuring prompts in XML can improve performance due to large language models being trained on web data, which includes HTML (a subset of XML).
• Reusable prompts should have clear instructions and defined variables to solve specific problems effectively.
• Level 3 prompts benefit from concrete examples to steer the output.
• Level 4 prompts are integrated into applications and can have dynamic variables for scalability.
• Maintaining a personal prompt library is beneficial for quick access and use of prompts across different applications.