prompt-engineering

Prompt Engineering

Universal techniques for crafting effective prompts across any LLM.

Core Principles

1. Structure with XML Tags

Use XML tags to create clear, parseable prompts:

<context>Background information here</context>
<instructions>
1. First step
2. Second step
</instructions>
<examples>Sample inputs/outputs</examples>
<output_format>Expected structure</output_format>

Benefits:

• Clarity: Separates context, instructions, and examples
• Accuracy: Prevents model from mixing up sections
• Flexibility: Easy to modify individual parts
• Parseability: Enables structured output extraction

Best practices:

• Use consistent tag names throughout (<instructions>, not sometimes <steps>)
• Reference tags explicitly: "Using the data in <context> tags..."
• Nest tags for hierarchy: <examples><example id="1">...</example></examples>
• Combine with other techniques: <thinking> for chain-of-thought, <answer> for final output

2. Control Output Shape

Specify explicit constraints on length, format, and structure:

<output_spec>
- Default: 3-6 sentences or ≤5 bullets
- Simple yes/no questions: ≤2 sentences
- Complex multi-step tasks:
  - 1 short overview paragraph
  - ≤5 bullets: What changed, Where, Risks, Next steps, Open questions
- Use Markdown with headers, bullets, tables when helpful
- Avoid long narrative paragraphs; prefer compact structure
</output_spec>

3. Prevent Scope Drift

Explicitly constrain what the model should NOT do:

<constraints>
- Implement EXACTLY and ONLY what is requested
- No extra features, components, or embellishments
- If ambiguous, choose the simplest valid interpretation
- Do NOT invent values, make assumptions, or add unrequested elements
</constraints>

4. Handle Ambiguity Explicitly

Prevent hallucinations and overconfidence:

<uncertainty_handling>
- If the question is ambiguous:
  - Ask 1-3 precise clarifying questions, OR
  - Present 2-3 plausible interpretations with labeled assumptions
- When facts may have changed: answer in general terms, state uncertainty
- Never fabricate exact figures or references when uncertain
- Prefer "Based on the provided context..." over absolute claims
</uncertainty_handling>