Skip to main content

Instruction-following Prompts

Instruction-following prompts are designed for models trained to follow explicit instructions, often leveraging reinforcement learning from human feedback (RLHF) or instruction tuning. These prompts enable more reliable, controllable, and user-aligned outputs.

Key Concepts​

  • Explicit Instructions: The prompt directly tells the model what to do.
  • Instruction-tuned Models: Models trained to follow instructions (e.g., via RLHF).
  • User Alignment: Outputs are tailored to user intent and requirements.
  • Controllability: The model’s behavior is more predictable and consistent.

Best Practices​

  1. Be Clear and Direct

    • Use unambiguous language.
    • Specify the task, format, and any constraints.

  2. Leverage Model Capabilities

    • Use instruction-following models for tasks requiring compliance.
    • Test with different phrasings to optimize results.

  3. Combine with Examples

    • Pair instructions with one-shot or few-shot examples for clarity.
    • Show both correct and incorrect outputs if needed.

  4. Iterate and Refine

    • Adjust instructions based on model performance.
    • Use feedback to improve prompt effectiveness.

Examples​

Basic Instruction-following​

Write a summary of the following article in three sentences or less.
[Insert article text here]

Formatting and Style​

List the main points from the text below. Respond in bullet points.
Text: "Exercise improves mood, boosts energy, and supports heart health."

Task-specific Instruction​

Translate the following sentence to German, using formal language.
Sentence: "How are you today?"

Multi-step Instruction​

First, extract all dates from the text. Then, summarize the main event for each date.
Text: "The conference was held on March 10th. The results were published on April 2nd."

Common Pitfalls​

  1. Vague or Ambiguous Instructions

    • Not specifying the desired output format or style.
    • Leaving room for interpretation that leads to inconsistent results.

  2. Overly Complex Instructions

    • Combining too many tasks in one prompt.
    • Making the prompt difficult for the model to follow.

  3. Ignoring Model Limitations

    • Expecting the model to follow instructions beyond its capabilities.
    • Not testing for edge cases or ambiguous scenarios.

  4. Lack of Feedback

    • Not refining prompts based on model outputs.
    • Failing to provide corrective examples.

Use Cases​

  1. Summarization

    • Article or document summaries
    • Meeting notes

  2. Data Extraction

    • Entity or fact extraction
    • Structured data generation

  3. Translation and Paraphrasing

    • Language translation
    • Rewriting for tone or style

  4. Task Automation

    • Step-by-step instructions
    • Workflow guidance

When to Use Instruction-following Prompts​

Instruction-following prompts are ideal when:

  • The task requires precise compliance with user instructions.
  • Consistency and predictability are important.
  • The model is instruction-tuned or RLHF-trained.
  • User alignment is a priority.

When to Consider Alternatives​

Consider other techniques when:

  • The task benefits from demonstration (use one-shot or few-shot).
  • The model is not instruction-tuned and struggles with compliance.
  • Open-ended or creative outputs are desired.

Tips for Optimization​

  1. Iterative Testing

    • Refine instructions based on model outputs.
    • Test with different phrasings for clarity.

  2. Combine with Examples

    • Use examples to reinforce instructions.
    • Show both correct and incorrect outputs if needed.

  3. Monitor for Drift

    • In multi-turn conversations, restate instructions as needed.
    • Remind the model of constraints if it deviates.

  4. Validate Outputs

    • Check for compliance with instructions.
    • Revise prompts if results are inconsistent or off-target.