prompt-engeneeringlisted

# 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: ```xml <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: ```xml <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 constrai