Prompt engineering: Quick Reference — Experimentation (NVIDIA-Certified Associate: Generative AI LLM)

Prompt Engineering Quick Reference for NVIDIA-Certified Associate: Generative AI LLM

This quick reference provides essential facts, definitions, and best practices for prompt engineering, a critical skill for developing and integrating AI-driven applications using large language models (LLMs).

Key Definitions

• Prompt: The input text or query given to an LLM to generate a response.
• Prompt Engineering: The process of designing and refining prompts to elicit accurate, relevant, and high-quality outputs from LLMs.
• Context Window: The maximum length of input tokens an LLM can process at once.
• Few-shot Prompting: Providing examples within the prompt to guide the model’s output.
• Zero-shot Prompting: Asking the model to perform a task without examples.

Prompt Engineering Best Practices

• Be Clear and Specific: Use precise language and explicit instructions to reduce ambiguity.
• Use Examples: Incorporate few-shot examples to demonstrate the desired output format or style.
• Control Length: Keep prompts concise but informative to fit within the model’s context window.
• Iterate and Test: Experiment with variations to optimize output quality.
• Use System and User Roles: When supported, separate instructions (system) from user queries to guide behavior.

Common Prompt Engineering Techniques

• Instruction Tuning: Explicitly instruct the model on the task (e.g., "Summarize the following text in 3 sentences.").
• Chain-of-Thought Prompting: Encourage step-by-step reasoning by prompting the model to explain its thought process.
• Template-Based Prompts: Use fixed prompt structures with variable placeholders for dynamic input.
• Prompt Chaining: Break complex tasks into multiple prompts executed sequentially.

Rules for Effective Prompt Engineering

• Avoid Ambiguity: Ambiguous prompts lead to inconsistent outputs.
• Leverage Model Strengths: Tailor prompts to the model’s known capabilities and limitations.
• Minimize Bias: Frame prompts neutrally to reduce biased or harmful outputs.
• Test Across Tasks: Validate prompts on diverse inputs to ensure robustness.

Quick Tips

• Use explicit keywords like "List," "Explain," or "Compare" to guide output style.
• Include output format instructions (e.g., "Provide a bullet list.").
• When outputs are too long or off-topic, shorten or clarify the prompt.
• Use negative prompts to exclude unwanted content if supported.

Worked Example

Task: Generate a brief summary of a news article.

Poor Prompt: "Tell me about this news article." (Likely vague and inconsistent)

Improved Prompt: "Summarize the following news article in 3 sentences, focusing on the main events and key figures."

Result: More focused, concise, and relevant summary output.

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