Unified Health Aid Podcast
What is Prompt Engineering? * Crafting effective prompts to guide Large Language Models (LLMs) toward accurate, useful outputs. * It's iterative: experimenting, evaluating, and refining prompts is crucial. Key Elements of Effective Prompt Engineering 1. LLM Output Configuration Configure the model settings effectively: * Output Length: More tokens = higher cost and latency. * Temperature: Controls randomness. * Lower temperatures (0.0 - 0.3) → More deterministic and focused results. * Higher temperatures (>0.7) → More creative and varied outputs. * Top-K: Limits sampling to the K highest-probability tokens. * Top-P (nucleus sampling): Samples from top tokens until cumulative probability P is reached. Recommended default configurations: * Balanced results: Temperature 0.2, top-P 0.95, top-K 30. * More creative: Temperature 0.9, top-P 0.99, top-K 40. * Deterministic results: Temperature 0.0 (useful for math problems). 2. Prompting Techniques Zero-shot Prompting * Provide simple instructions without examples. * Good for straightforward tasks. One-shot & Few-shot Prompting * Include one or more examples within the prompt. * Enhances accuracy and consistency, particularly useful for complex or structured tasks. System, Contextual, and Role Prompting * System prompting: Defines the overall task context and constraints (e.g., format outputs as JSON). * Contextual prompting: Offers additional context for precise results. * Role prompting: Assigns the model a persona or role (teacher, comedian, travel guide, etc.), shaping its tone and content. Step-back Prompting * Start broadly, then narrow down specifics to enhance contextual accuracy. * Helps models reason effectively. Chain of Thought (CoT) Prompting * Encourages LLMs to explain reasoning steps explicitly (e.g., math problems). * Significantly improves accuracy and interpretability. Self-consistency * Run the same prompt multiple times at higher temperatures, then choose the most common response. * Good for reasoning and classification tasks. Tree of Thoughts (ToT) * Extends CoT by simultaneously exploring multiple reasoning paths. * Effective for complex tasks needing deep exploration. ReAct (Reason & Act) * Combines reasoning with external tool usage (like search engines) for better problem-solving. * Useful for factual queries requiring external validation or data. 3. Automatic Prompt Engineering * Automating prompt creation by prompting an LLM to generate multiple potential prompts. * Evaluate and select the best-performing prompt using metrics like BLEU or ROUGE scores. 4. Code Prompting Techniques * LLMs can write, explain, translate, debug, and review code. * Clearly instruct models on desired programming languages and outcomes. * Test and verify the generated code for correctness. 5. Multimodal Prompting * Involves using multiple formats (text, images, audio) in prompts. * Enhances clarity and context (dependent on model capabilities). Best Practices for Prompt Engineering General Tips * Provide clear, concise instructions. * Include relevant examples: One-shot or few-shot examples dramatically improve performance. * Design simple prompts: Avoid overly complex language or irrelevant information. * Be specific about outputs: Clearly state expected results (structure, format, content). * Favor positive instructions over negative constraints. Controlling Output * Explicitly instruct output length or style when necessary (e.g., "Explain quantum physics in a tweet-length message"). Variables in Prompts * Use dynamic variables to easily adapt prompts (e.g., {city} → "Amsterdam"). Input and Output Formats * JSON is recommended for structured outputs to minimize hallucinations and increase reliability. * JSON Schemas can help structure inputs, defining clear expectations for LLMs. Iterative Development * Continuously test, refine, and document prompts. * Record prompt versions, configurations, model outputs, and feedback for reference and improvement. Chain of Thought Specific Tips * Always put the reasoning steps before the final answer. * Set temperature to 0 for reasoning-based tasks to ensure deterministic responses. Prompt Documentation Use this structured format to document prompt attempts for easy management and future reference: FieldDetails to includeNamePrompt name/versionGoalSingle-sentence description of the prompt’s purposeModelModel name/versionTemperatureValue (0.0 - 1.0)Token LimitNumeric limitTop-KNumeric settingTop-PNumeric settingPromptFull text of the promptOutputGenerated output(s) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.uhaid.org [https://www.uhaid.org?utm_medium=podcast&utm_campaign=CTA_1]
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