BEST Video I’ve seen on Prompt Engineering (With Notes)
I. Introduction
- Objective: Understand the concept of prompt engineering with GPT and its importance.
- Brief history: We've been tinkering with prompt engineering since GPT-2.
- There are three main types of prompts to explore.
II. Three Fundamental Operations
- Reductive Operations: These are operations that simplify or extract essential aspects from text.
- Summarization: Rephrase information in fewer words. This can be in the form of lists, nodes, or an executive summary.
- Distillation: Get to the core principle or fact.
- Extraction: Used commonly in earlier NLP to answer questions or pull out specific details like dates or numbers.
- Characterizing: Understand the true nature or essence of a text, be it through structural or rhetorical analysis.
- Evaluations: Measure or judge content, perhaps based on set criteria, moral viewpoints, etc.
- Critiquing: Give feedback and suggest improvements.
- Transformational Operations: These modify the presentation or structure of text.
- Reformatting: Change how content is presented, such as turning a paragraph into bullet points or prose into a screenplay format.
- Refactoring: Similar to programming, it means saying the same thing but in a different way.
- Language Change: Translation between languages or even between programming languages.
- Restructuring: Reorder, add, or delete parts of content.
- Modification: Change the intention, tone, style, etc., of a text.
- Clarification: Make text more straightforward and easy to understand.
- Generative Operations: Create new content from instructions or ideas.
- Drafting: Write new documents based on provided guidelines.
- Planning: Create plans for action or projects.
- Brainstorming: Generate a list of potential ideas or solutions.
- Problem Solving: Think through issues, brainstorm, and formulate hypotheses.
- Amplification: Dive deeper into a subject or "unpack" it for more detail.
III. Prompt Engineering Insights
- Bloom's Taxonomy: A way to understand human learning processes.
- Levels:
- Remember: Simply recall facts.
- Understand: Explain concepts or ideas.
- Apply: Utilize info in unfamiliar situations.
- Analyze: Connect various ideas together.
- Evaluate: Provide reasoning for decisions or actions.
- Create: Craft new, unique works.
- Latency and Emergence: Understand how GPT retrieves and presents information.
- Latent Content: This is knowledge the model has, waiting to be prompted.
- Comes from its training data.
- Can bring together and create new details.
- Includes general facts, culture, history, and more.
- Emerging Capabilities: Bigger models (like later GPT versions) show more of these unique abilities, offering insights and outputs that aren't explicitly in their training data.
Conclusion:
Prompts help guide GPT models in generating specific outputs. Understanding these different operations and how they interact with the model's latent knowledge can lead to more refined and useful results. As AI models evolve, our understanding of their capabilities and potential expands.
