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)

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