Every prompt created should be clear, specific, and optimized for the target model.

<quick_start>

• Clarify purpose: What should the prompt accomplish?

• Identify model: Claude, GPT, or other (techniques vary slightly)

• Select techniques: Choose from core techniques based on task complexity

• Structure content: Use XML tags (Claude) or markdown (GPT) for organization

• Add examples: Include few-shot examples for format-sensitive outputs

• Define success: Add clear success criteria

• Test and iterate: Refine based on outputs

<core_structure> Every effective prompt has:

<context> Background information the model needs </context>

<task> Clear, specific instruction of what to do </task>

<requirements>

• Specific constraints
• Output format
• Edge cases to handle </requirements>

<examples> Input/output pairs demonstrating expected behavior </examples>

<success_criteria> How to know the task was completed correctly </success_criteria>

</core_structure> </quick_start>

<core_techniques>

Priority: Always apply first

• State exactly what you want

• Avoid ambiguous language ("try to", "maybe", "generally")

• Use "Always..." or "Never..." instead of "Should probably..."

• Provide specific output format requirements

See: references/clarity-principles.md

Claude was trained with XML tags. Use them for:

• Separating sections: <context> , <task> , <output>

• Wrapping data: <document> , <schema> , <example>

• Defining boundaries: Clear start/end of sections

See: references/xml-structure.md

Provide 2-4 input/output pairs:

<examples> <example number="1"> <input>User clicked signup button</input> <output>track('signup_initiated', { source: 'homepage' })</output> </example> </examples>

See: references/few-shot-patterns.md

Add explicit reasoning instructions:

• "Think step by step before answering"

• "First analyze X, then consider Y, finally conclude Z"

• Use <thinking> tags for Claude's extended thinking

See: references/reasoning-techniques.md

System prompts set the foundation:

• Define Claude's role and expertise

• Set constraints and boundaries

• Establish output format expectations

See: references/system-prompt-patterns.md

Start Claude's response to guide format:

Assistant: {"result":

Forces JSON output without preamble.

For Claude 4.5 with context awareness:

• Inform about automatic context compaction

• Add state tracking (JSON, progress.txt, git)

• Use test-first patterns for complex implementations

• Enable autonomous task completion across context windows

See: references/context-management.md

</core_techniques>

<prompt_creation_workflow> <step_0> Gather requirements using AskUserQuestion:

What is the prompt's purpose?

• Generate content

• Analyze/extract information

• Transform data

• Make decisions

• Other

What model will use this prompt?

• Claude (use XML tags)

• GPT (use markdown structure)

• Other/multiple

What complexity level?

• Simple (single task, clear output)

• Medium (multiple steps, some nuance)

• Complex (reasoning, edge cases, validation)

Output format requirements?

• Free text

• JSON/structured data

• Code

• Specific template </step_0>

<step_1> Draft the prompt using this template:

<context> [Background the model needs to understand the task] </context>

<objective> [Clear statement of what to accomplish] </objective>

<instructions> [Step-by-step process, numbered if sequential] </instructions>

<constraints> [Rules, limitations, things to avoid] </constraints>

<output_format> [Exact structure of expected output] </output_format>

<examples> [2-4 input/output pairs if format matters] </examples>

<success_criteria> [How to verify the task was done correctly] </success_criteria>

</step_1>

<step_2> Apply relevant techniques based on complexity:

• Simple: Clear instructions + output format

• Medium: Add examples + constraints

• Complex: Add reasoning steps + edge cases + validation </step_2>

<step_3> Review checklist:

• Is the task clearly stated?

• Are ambiguous words removed?

• Is output format specified?

• Are edge cases addressed?

• Would a person with no context understand it? </step_3> </prompt_creation_workflow>

<anti_patterns>

❌ "Help with the data" ✅ "Extract email addresses from the CSV, remove duplicates, output as JSON array"

See: references/anti-patterns.md </anti_patterns>

<reference_guides> Core principles:

• references/clarity-principles.md - Being clear and direct

• references/xml-structure.md - Using XML tags effectively

Techniques:

• references/few-shot-patterns.md - Example-based prompting

• references/reasoning-techniques.md - Chain of thought, step-by-step

• references/system-prompt-patterns.md - System prompt templates

• references/context-management.md - Context windows, long-horizon reasoning, state tracking

Best practices by vendor:

• references/anthropic-best-practices.md - Claude-specific techniques

• references/openai-best-practices.md - GPT-specific techniques

Quality:

• references/anti-patterns.md - Common mistakes to avoid

• references/prompt-templates.md - Ready-to-use templates </reference_guides>

<success_criteria> A well-crafted prompt has:

• Clear, unambiguous objective

• Specific output format with example

• Relevant context provided

• Edge cases addressed

• No vague language (try, maybe, generally)

• Appropriate technique selection for task complexity

• Success criteria defined </success_criteria>