Prompt creator

Mindset

Before writing a single word, ask yourself:

1. What is the ONE job? Every prompt should have exactly one clear objective. Two jobs = two prompts.
2. Who consumes the output? Human (optimize readability) vs LLM (optimize parseability with XML/JSON) vs API (optimize structure).
3. What does failure look like? Design the prompt to make the most common failure mode impossible.

Technique Selection — Decision Tree

Don't pick techniques by habit. Pick by task characteristics:

Task signal	Technique	Why
Output format is critical	Few-shot examples (2-3 pairs)	Examples communicate format better than 100 words of description
Multiple valid approaches exist	Extended thinking / CoT	Forces evaluation before commitment
Output must be exactly X format	Prefill (Claude) or JSON mode (GPT)	Eliminates preamble, forces structure
Task has subtle edge cases	Boundary examples in few-shot	Show the tricky cases, not the obvious ones
Complex multi-step reasoning	Decompose into sequential prompts	One complex prompt < two simple prompts chained
Output quality varies wildly	Add rubric / evaluation criteria	Model self-calibrates against explicit standards
Model keeps ignoring instructions	Move constraint to system prompt + repeat in user	Dual-placement beats single placement

Prompt Architecture — What Goes Where

This is the #1 thing people get wrong. Placement matters more than wording:

SYSTEM PROMPT (persistent identity layer)
├── Role + expertise domain (1-2 sentences max)
├── Hard constraints (NEVER / ALWAYS rules)
├── Output format defaults
└── Tone / style baseline
    ↕ DO NOT put task-specific instructions here

USER MESSAGE (task layer)
├── Context the model needs for THIS task
├── The specific objective
├── Input data / content to process
├── Output format if different from default
└── Edge cases specific to this input

ASSISTANT PREFILL (output steering — Claude only)
├── Force JSON: `{"result":`
├── Force list: `1.`
└── Force language: Start with target language text

Critical rule: System prompts should be STABLE across conversations. If you're changing it per request, you're putting task instructions in the wrong layer.

Model-Specific Differences That Actually Matter

Dimension	Claude	GPT	Gemini
Structure	XML tags (<context>, <task>) — trained on them	Markdown headers + numbered lists	Markdown, tolerates XML
Constraint framing	Positive framing works better ("Write in plain language" > "Don't use jargon")	Negative constraints work fine	Either works
Format enforcement	Prefill the assistant response	response_format: { type: "json_object" }	System instruction + example
Long instructions	Handles very long system prompts well (200K context)	Degrades past ~4K system prompt	Good with long context
Extended thinking	thinking blocks, trigger with "Thoroughly analyze..."	Not available natively	"Think step by step" in prompt
Tool calling	inputSchema/outputSchema (MCP-aligned)	parameters (JSON Schema)	function_declarations

NEVER

• NEVER put role-play AND constraints AND format AND examples AND CoT all in one prompt — pick 2-3 techniques max. Kitchen-sink prompts confuse the model.
• NEVER describe format in words when you can show an example. "Output a JSON object with keys name, age, and score" < showing {"name": "Alice", "age": 30, "score": 95}.
• NEVER use vague hedging: "try to", "maybe", "generally", "if possible". These give the model permission to skip the instruction.
• NEVER add a role that contradicts the task. "You are a friendly assistant" + "Respond with only JSON, no prose" = conflict.
• NEVER ask the model to "not think about X" — it focuses attention on X. Reframe positively.
• NEVER use examples that all look the same — include edge cases. If all 3 examples are happy-path, the model only learns the happy path.
• NEVER change prompt AND model AND temperature simultaneously when debugging — change one variable at a time.

Prompt Failure Diagnosis

When a prompt produces bad output, diagnose before rewriting:

Symptom	Likely cause	Fix
Model ignores an instruction	Instruction buried in long text	Move to system prompt or add "CRITICAL:" prefix
Output format is wrong	No example of desired format	Add 1-2 concrete examples
Model hallucinates facts	No grounding data provided	Add <context> with source material
Output too verbose	No length constraint	Add "Maximum N sentences/lines/tokens"
Model hedges ("I think maybe...")	Role is too passive	Set confident role: "You are an expert who gives direct answers"
Inconsistent quality across runs	Temperature too high or prompt is ambiguous	Lower temperature AND add specificity
Model adds unsolicited caveats	No instruction about caveats	Add "Do not add disclaimers or caveats"
Wrong level of detail	No audience specified	Add "Write for [audience]"

Workflow

1. Ask (use AskUserQuestion): purpose, target model, output consumer, failure tolerance
2. Architect: decide system vs user split, select 2-3 techniques from decision tree
3. Draft: write the prompt, starting with output format example
4. NEVER test: review against the NEVER list above
5. Edge-case: add 1-2 boundary examples that show tricky cases
6. Ship: deliver the prompt with a test suggestion ("Try it with this input: ...")

References

MANDATORY — read before creating prompts for specific models:

• references/anthropic-best-practices.md — Claude-specific: XML, prefill, extended thinking, context management
• references/openai-best-practices.md — GPT-specific: JSON mode, function calling

Load on demand:

• references/prompt-templates.md — Ready-to-adapt templates (analysis, transformation, generation, code review)
• references/context-management.md — Long-horizon tasks, state tracking, multi-session patterns
• references/anti-patterns.md — Extended anti-pattern catalog with examples