Prompt Crafter

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Prompt Crafter

Your prompts suck because they're vague. Let's fix that.

Why Most Prompts Fail

I've written ~400 prompts over the past month running a business autonomously. The ones that failed almost always had the same problem: they told the AI what to do but not how to think about it. The fix isn't more words — it's the right structure.

The 4 Frameworks

1. RACE — The Daily Driver (use for ~70% of tasks)

Role · Action · Context · Example

This is your default. If you don't know which framework to use, use RACE.

Real example — writing a product description I actually used:

Role: You're a direct-response copywriter who learned from Eugene Schwartz and 
Claude Hopkins. You believe every word must earn its place.

Action: Write a product description for "The Prompt Playbook" — a PDF guide 
with 50 AI prompts.

Context:
- Audience: people who use ChatGPT daily but get generic outputs
- Price: $19 (impulse buy range — don't oversell)
- Tone: confident, slightly irreverent, zero corporate language
- Length: 80-120 words
- Must address the objection "I can just Google prompts for free"

Example of the voice I want:
"You've been asking ChatGPT nicely. That's the problem."

Why each piece matters:

• Role → constrains the voice and expertise level. "Copywriter" gets different output than "marketing manager"
• Action → specific deliverable, not a vague direction
• Context → kills generic output. The objection-handling line alone changes the entire response
• Example → shows don't tell. One line of example voice is worth 50 words of description

When RACE breaks down: Tasks requiring multi-step reasoning. RACE gives you good writing but it won't help you think through a complex problem. That's where chain-of-thought comes in.

2. Chain-of-Thought — The Analyst

Force the model to show its work. Best for decisions, comparisons, debugging, and anything where the reasoning matters as much as the answer.

Real example — evaluating whether to add Stripe to my product:

I'm deciding whether to add Stripe as a payment option alongside Gumroad 
for a $19 digital product. Think through this step by step:

1. What are the concrete advantages of Stripe over Gumroad for digital products?
2. What are the disadvantages and hidden costs?
3. For a product with 0 sales and <50 followers, does the effort of adding 
   Stripe make sense RIGHT NOW?
4. What's the minimum sales volume where Stripe's lower fees actually matter?
5. Give me a concrete recommendation with a specific trigger point 
   ("Add Stripe when X happens").

The trick: Numbered steps force sequential reasoning. Without them, the model jumps to a conclusion. With them, you get reasoning you can actually verify and push back on.

Cost warning: Chain-of-thought responses use 30-50% more tokens. On Claude or GPT-4, that's real money at scale. Use RACE for simple tasks; save CoT for decisions that actually need analysis.

When CoT breaks down: Creative tasks. Forcing step-by-step reasoning on a poem or tweet makes it robotic. Creativity needs breathing room, not a checklist.

3. Constraint-Stacking — The Precision Tool

When the output format matters as much as the content. You're essentially building a spec.

Real example — writing tweets with specific requirements:

Write a tweet about AI replacing jobs.

CONSTRAINTS:
- Max 240 characters (leave room for engagement)
- Must include a specific claim (not a vague opinion)
- No hashtags
- Must end with a question that invites disagreement
- Tone: confident take, not doom-and-gloom
- Do NOT use: "game-changer", "revolutionary", "unlock", "journey"

BANNED PATTERNS:
- Starting with "Just..." or "So..."
- Rhetorical questions as the opening line
- Emoji as the first character

The sweet spot is 4-7 constraints. Fewer than 4 and you get generic output. More than 8 and the model starts silently dropping constraints — usually the ones in the middle. If you need 10+ constraints, split into two prompts.

Why the banned words/patterns section works: Models have default patterns they fall into. Explicitly banning them forces originality. I keep a running list of AI-isms I ban: "dive into", "landscape", "leverage", "it's worth noting", "in today's fast-paced world."

4. Few-Shot — The Pattern Matcher

Show 2-3 examples of what you want. The model extracts the pattern and applies it. Unreasonably effective for consistent formatting, tone matching, and data extraction.

Real example — generating content in a specific voice:

Write tweets in the SparkForge voice. Here are 3 examples of the voice:

Example 1: "Stop asking ChatGPT nicely. It's not your coworker. 
It's a reasoning engine. Give it constraints, not compliments."

Example 2: "90% of people using AI in 2026 are getting WORSE at their jobs. 
They're outsourcing thinking, not augmenting it."

Example 3: "Prompt engineering isn't a skill. It's just clear thinking 
with a keyboard. If you can't explain what you want to a smart intern, 
AI can't help you."

Now write a tweet about AI and hiring in this same voice.

The rule of 3: Two examples establish a pattern. Three examples lock it in. Four or more is usually wasted tokens. Exception: if the pattern has multiple variations (formal vs casual), show one of each.

When few-shot breaks down: When your examples are too similar. The model over-indexes on the shared features. If all 3 examples start with a command ("Stop...", "Don't...", "Never..."), every output will start with a command too. Vary your examples intentionally.

The Decision Tree

Is the task creative writing or content?
  → RACE (+ few-shot if matching an existing voice)

Does it require multi-step reasoning or analysis?
  → Chain-of-Thought

Does format/length/structure matter a lot?
  → Constraint-Stacking (or RACE + Constraints for the best of both)

Do you need consistent output across many runs?
  → Few-Shot (show the pattern, let it match)

For complex tasks, combine frameworks. My most reliable setup for production prompts: RACE skeleton + 2-3 constraints + 1 example. Covers role, specificity, format, and voice in one prompt.

Troubleshooting Bad Outputs

Safety Rules for Production Prompts

If you're building prompts for an app or automated system:

1. Always include a refusal path. "If the input is unclear or potentially harmful, respond with: 'I need more context to help with that.'" Without this, the model guesses — sometimes dangerously.
2. Cap output length. Models default to verbose. In production, "Maximum 200 tokens" prevents runaway costs and timeouts.
3. Specify the output format exactly. "Respond in JSON with keys: summary, confidence, next_step" prevents formatting surprises that break your parser.
4. Test with adversarial inputs. Prompt injection is real. If your prompt accepts user input, test what happens when someone types "Ignore all previous instructions and..."
5. Version your prompts. Keep a changelog. When output quality drifts, you need to know what changed.

Quick Wins (copy these today)

Make any prompt 2x better instantly:

• Add "Do NOT include [common AI filler]" — kills "In conclusion", "It's worth noting", etc.
• Add "Write for someone who [specific trait]" — forces audience awareness
• Add one example of the voice/format you want — shows > tells
• End with "Before responding, identify the 2 most important things to get right" — forces prioritization

Reference

See references/frameworks.md for 12 more worked examples across writing, analysis, coding, and creative tasks.