$2.4M of Prompt Engineering Hacks in 53 Mins (GPT, Claude)

6 Years of Prompt Engineering in 53 Minutes - Summary

Key Concepts:

• API Playground/Workbench vs. Consumer Models
• Prompt Length vs. Model Performance
• System, User, and Assistant Prompts
• Zero-Shot, One-Shot, Few-Shot Prompting
• Conversational Engines vs. Knowledge Engines
• Unambiguous Language
• Iterative Prompting with Data
• Explicit Output Format Definition
• Removing Conflicting Instructions
• Temperature and Randomness

1. API Playground/Workbench vs. Consumer Models

• Main Point: Using API playground/workbench versions of LLMs (e.g., platform.openai.com/playground) provides more control and better prompt engineering capabilities compared to consumer models like ChatGPT or Claude.
• Details: Consumer models have hidden, pre-set configurations that limit user control. API versions allow manipulation of parameters like model type, response format, functions, temperature, max tokens, stop sequences, top P, frequency penalty, and presence penalties.
• Actionable Insight: Immediately switch to using API playground/workbench versions for more effective prompt engineering.

2. Prompt Length vs. Model Performance

• Main Point: Model performance decreases with increasing prompt length.
• Details: A graph illustrates that accuracy decreases as input text (prompt) length increases. For example, GPT-4's accuracy drops almost 20% as prompt length increases.
• Actionable Insight: Shorten prompts by improving information density. Use "AO obfuscation, espo elucidation" (Keep It Simple Stupid) to reduce verbosity without losing essential instructions.
• Example: A 674-word prompt was reduced to a shorter, more concise version while maintaining the same meaning, resulting in an estimated 5% accuracy improvement.

3. System, User, and Assistant Prompts

• Main Point: Understanding the roles of system, user, and assistant prompts is crucial for advanced prompting.
• System Prompt: Defines the model's identity and role (e.g., "You are a helpful intelligent assistant").
• User Prompt: Provides the actual instruction to the model (what you want it to do).
• Assistant Prompt: The model's output, which can be used as an example to guide future outputs.
• Actionable Insight: Use the assistant prompt to reinforce desired behavior by providing feedback (e.g., "Fantastic work") and then requesting a similar task.

4. Zero-Shot, One-Shot, Few-Shot Prompting

• Main Point: One-shot prompting (providing one example) offers a disproportionately large improvement in accuracy compared to zero-shot or few-shot prompting.
• Details: A study showed a significant accuracy gap between zero-shot and one-shot prompting, larger than the gap between one-shot and few-shot (e.g., 20 examples).
• Actionable Insight: For mission-critical tasks, always include at least one example in the prompt to guide the model.
• Goldilock Zone: One-shot prompting combined with short prompt length is the optimal approach.

5. Conversational Engines vs. Knowledge Engines

• Main Point: LLMs are conversational engines, not knowledge engines. They are good at reasoning and conversation but not at providing precise factual information.
• Details: LLMs can approximate answers based on patterns learned from vast amounts of text, but they don't "know" exact facts.
• Knowledge Engines: Databases, encyclopedias, and Google Sheets are knowledge engines that store facts but lack conversational abilities.
• Retrieval Augmented Generation (RAG): The best approach is to connect an LLM to a knowledge engine (e.g., using RAG) to retrieve accurate data and then use the LLM to generate a response.
• Actionable Insight: Don't rely on LLMs for precise factual information unless they are connected to an external knowledge base.

6. Unambiguous Language

• Main Point: Using unambiguous language is crucial to reduce variability in model outputs and ensure consistent results.
• Details: LLMs are creative, leading to different outputs for the same prompt.
• Actionable Insight: Be specific and avoid vague terms. Instead of "produce a report," specify "list our five most popular products and write me a one-paragraph description." Provide examples of the desired output format.

7. Spartan Tone of Voice

• Main Point: Using the term "Spartan" to describe the desired tone of voice can improve prompt effectiveness.
• Actionable Insight: Include "Use a Spartan tone of voice" in your prompts to encourage direct, pragmatic, and concise responses.

8. Iterative Prompting with Data

• Main Point: Iteratively test and refine prompts using data to ensure reliable and consistent outputs.
• Monte Carlo Approach: Test prompts multiple times and progressively make changes to improve accuracy.
• Process:
  1. Create a Google Sheet with columns for "Prompt," "Output," and "Good Enough."
  2. Generate multiple outputs (e.g., 10-20) for each prompt.
  3. Evaluate each output and mark whether it is "good enough" for the intended use case.
  4. Calculate the percentage of "good enough" outputs for each prompt.
  5. Compare the results and choose the prompt with the highest accuracy score.
• Actionable Insight: Don't rely on a single successful output. Test prompts rigorously and use data to drive improvements.

9. Explicit Output Format Definition

• Main Point: Explicitly define the desired output format to ensure the model generates data in a usable structure.
• Examples:
  • "Output a bulleted list."
  • "Output JSON."
  • "Generate a CSV with month, revenue, and profit headings based off of the below data."
• Actionable Insight: Specify the exact output format (e.g., JSON, CSV, bulleted list) to facilitate integration with code, servers, scripts, and other applications.

10. Removing Conflicting Instructions

• Main Point: Remove any conflicting or contradictory instructions within the prompt to ensure clarity and consistency.

Synthesis/Conclusion

Effective prompt engineering involves understanding the underlying mechanisms of LLMs, using the right tools (API playground/workbench), and employing data-driven iterative testing. By focusing on concise, unambiguous language, defining clear output formats, and leveraging techniques like one-shot prompting, users can significantly improve the quality and reliability of LLM outputs for various business applications. The key is to treat LLMs as conversational engines that require precise guidance and validation, rather than as infallible sources of knowledge.