Prompt Engineering Best Practices

Summary

• This recording was a comprehensive and high-density masterclass on prompt engineering for AI, particularly aimed at business applications.
• The presenter, Nick, shared practical advice drawn from his six years of AI experience, including lessons learned from building several profitable businesses using prompt engineering.
• The session covered foundational principles of how language models work, actionable best practices for prompt construction, and strategies for maximizing prompt reliability and output quality.
• Key recommendations, prompt structuring techniques, and advanced prompting insights were outlined, targeting both beginners and advanced users.

Action Items

• None noted (the session was informational; no outstanding owner-assigned tasks were mentioned).

Advanced Prompt Engineering Best Practices

• Use API Playground/Workbench Models: For serious prompt engineering, avoid consumer-facing interfaces like ChatGPT and Claude. Instead, use playground/workbench versions via API for greater control over model settings and prompt configuration.
• Shorter Prompts Improve Output: Model performance declines as prompt length increases. Highly verbose prompts reduce accuracy; condense prompts to improve information density and reduce token usage while maintaining relevant instructions.
• Minimize Verbosity: Remove unnecessary words and fluff. Write prompts as simply and succinctly as possible without sacrificing intent or clarity.
• Understand Prompt Types: Distinguish between system, user, and assistant prompts. Structure interactions by explicitly setting the system prompt (defining the model’s “identity”), the user prompt (instruction), and using assistant outputs as reference examples when possible.
• One or Few-Shot Prompting: Provide at least one example in your prompt. A single example yields a disproportionately large boost in model accuracy, more so than adding many more examples.
• Conversational vs. Knowledge Engines: Recognize that LLMs are best as conversational engines (for reasoning and structured dialogue), not as knowledge databases. For fact-based tasks, pair LLMs with external knowledge sources (RAG/retrieval-augmented generation).

Prompt Construction and Optimization Techniques

• Be Unambiguous: Use clear, precise language. Avoid vague instructions (“produce a report”) in favor of explicit tasks (“list 5 most popular products and write a one-paragraph description for each”).
• Spartan Tone: Specify “Spartan” tone for responses—direct, pragmatic, minimal fluff.
• Data-Driven Iteration: Test prompts with multiple outputs (e.g., generating 10-20 responses), measure which outputs meet business needs, and iteratively refine prompts using a data-driven approach (e.g., track pass/fail rates in a spreadsheet).
• Define Output Format Explicitly: Specify expected formats (bulleted lists, JSON, CSV, etc.) in prompts to facilitate direct integration with business tools and reduce post-processing work.
• Remove Conflicting Instructions: Eliminate contradictory requirements (e.g., “detailed summary”) that introduce ambiguity and reduce output quality.
• Learn Structured Data Formats: Familiarize yourself with XML, JSON, and CSV formats—enabling structured outputs that integrate well with other systems and help ensure consistency.
• Use a Consistent Prompt Structure: Structure all major prompts as follows:
  1. Context (background, user identity, scenario)
  2. Instructions (explicit task description)
  3. Output Format (desired structure and style)
  4. Rules (do’s and don’ts)
  5. Examples (reference input-output pairs)
• Use AI to Generate Examples for AI: If examples are needed for few-shot prompting, use the model itself to generate training examples for further prompt optimization.
• Pick the Right Model for the Task: Use more capable (and slightly more expensive) models for mission-critical business processes, as the marginal cost is usually negligible compared to improvements in reliability and quality.

Decisions

• Adopt condensed, unambiguous, and data-driven approaches to prompt engineering — Rationale: Shorter, example-based, and clearly formulated prompts reliably increase output quality and model accuracy, especially in business use cases.

Open Questions / Follow-Ups

• None explicitly mentioned; attendees are encouraged to comment with additional questions or requests for future deep-dives on specific topics.