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)
