Ultimate Prompt Engineering Playbook

• Ultimate Prompt Engineering Playbook
• What is Prompting?
• Why Prompting Matters
• How AI Thinks
• The C.L.E.A.R. Framework
• 🧱 Prompt Foundations
• ⚠️ TL;DR — Top 10 Tactics That Actually Work
• 🧬 Tiers of Prompting
• 1. Structured Prompting (“Training Wheels”)
• 2. Conversational Prompting
• 3. Meta Prompting
• 4. Reverse Meta Prompting
• 🧱 Prompt Building Blocks
• 🔁 Human-in-the-Loop, Chat Mode & Iteration
• 🔄 Reverse Prompting Template (Mark)
• 🧠 Debugging Prompts with Reasoning Models
• 🛠 Multi-Step Prompting Across Tools
• 🔐 Prompt Injection & Security Tactics (Sander)
• ❌ What Doesn’t Work (Sander + Mark)
• 🧵 Minimum Viable Prompting System (MVP)
• ✅ Full Session Template
• Conclusion
• Resources

What is Prompting?

Prompting = text instructions for an AI. It tells the AI what to do — build a UI, write logic, debug, whatever. The clearer and more structured the prompt, the better the output. Simple.

Why Prompting Matters

Prompt quality = output quality. Good prompting lets you:

• Automate tasks
• Debug faster
• Build workflows without micromanaging the AI

You don’t need to be technical, just precise. This playbook covers how.

How AI Thinks

LLMs don’t "understand" — they predict based on training data. Your prompt is everything. Key rules:

• Context matters: Tell it what it needs to know. Be specific.
• Be explicit: If you don’t say it, it won’t guess it. State all constraints.
• Structure counts: Front-load important info. Reiterate must-haves at the end. Keep prompts within the model’s context window.
• Know its limits: No real-time data. Confident guesses = hallucinations. Supply source data or check facts.
• Literal-minded intern: Treat the model like one. Spell it all out.

The C.L.E.A.R. Framework

Great prompts follow five principles. Think of it like a checklist:

• Concise – Get to the point. Don’t hedge. Don’t ramble.

Bad: “Could you maybe...”

Good: “Write a 200-word summary of how climate change affects coastal cities.”

Skip filler words. If it doesn’t help the AI act, drop it.

• Logical – Structure your prompt so it flows. Sequence complex asks.

Bad: “Build a signup page and show user stats.”

Good:

1. “Create a signup form using Supabase (email/password).”
2. “On success, display user count in dashboard.”

Clear order = clear results.

• Explicit – Tell the AI what you want and what you don’t want.

Bad: “Tell me about dogs.”

Good: “List 5 unique facts about Golden Retrievers, in bullet points.”

Add formatting, tone, style, examples — whatever makes it precise. Assume the model knows nothing.

• Adaptive – If the output sucks, change your prompt.

Feedback loop = power move. Example:

“The output is missing the auth step. Add Supabase login logic.”

Iterate like a dev fixing bugs.

• Reflective – Step back and ask: what made that prompt work? What failed?

Good prompt engineers document working patterns and prompt snippets. After a complex session, ask the AI:

“Summarize the solution and generate a reusable version of this prompt.”

That’s reverse meta prompting — and it compounds over time.

🧱 Prompt Foundations

Every well-structured prompt includes:

• Directive: Clear instruction (“Write a poem…”).
• Context: Domain knowledge, user story, project specifics.
• Examples / Few‑Shot: 2‑3 “good” Q/A examples or formatted snippets (consistent style—JSON, markdown, bullet).
• Output Format: Explicit (JSON, CSV, bullet list, code block).
• Constraints: Word limits, style guidelines, tone, channel-specific rules.
• Tools/Models: Which LLM to use (e.g. GPT‑4, Claude 3.5), environment notes (webhook, edge function).

⚠️ TL;DR — Top 10 Tactics That Actually Work

1. Few-Shot Prompting

Show 2–3 examples of what "good" looks like. Use consistent formatting (Q/A, markdown, JSON). Supercharges performance.

2. Decomposition

Break tasks into smaller steps. Ask: "What steps should we do first?" Great for agents, automation, reasoning-heavy tasks.

3. Self-Criticism Loop

Ask model to critique and improve its own output. Run 1–2 iterations. "How would you improve your answer? Now do it."

4. Add Relevant Context

Top-load your prompt with domain-specific info. Docs, user stories, bios, project data — raw is fine.

5. Ensembling

Prompt multiple agents with different roles/styles. Compare and combine outputs. Think: prompt version of a random forest.

6. Reverse Prompting Workflow (Mark)
1. Prompt interactively to build something (chat mode)
2. Debug issues together
3. Ask it to generate final reusable prompt incorporating learnings + edge cases
4. Store it, reuse it
7. Use Chat Mode Strategically

Ideal for:

1. Debugging live prompts
2. Asking clarifying questions
3. Getting unstuck when building something step-by-step
8. Prompting Across Tools (Mark)

Use prompting to coordinate UI (Lovable), logic (Make/n8n), and APIs. Prompts become glue:

1. UI: "Create PDF upload button"
2. Logic: "When PDF received, parse it"
3. API: "Store in Supabase and return response"
9. Reasoning Models for Debugging

Claude, Gemini, GPT-4 are great for root cause analysis. Paste error + logs and ask for explanation and solution.

🧬 Tiers of Prompting

Four levels. Each serves a purpose. Use whichever gets the job done.

1. Structured Prompting (“Training Wheels”)

Use a labeled, chunked format when:

• You’re starting out.
• The task is complex or multi-step.
• You want reliability.

Example:

pgsql
CopyEdit
Context: You’re a senior full-stack dev.
Task: Build a login page in React with Supabase (email/password).
Guidelines: Minimal UI with Tailwind. Add clear code comments.
Constraints: Modify only LoginPage component. Must work on mobile.

Why it works: no ambiguity. You’re forcing clarity — both for yourself and the model.

2. Conversational Prompting

No labels. Just well-written natural language.

Example:

I need a React component that uploads a profile picture. It should include a file input and submit button. On submit, upload to Supabase, update the user profile, and show success/error messages.

Use this once you’re confident you won’t forget key info. Add line breaks or bullets if needed. It’s faster, less rigid — but still requires discipline.

3. Meta Prompting

AI helps you write better prompts. Literally.

Example:

Here’s my last prompt. Improve it: make it more specific, structured, and concise.

Or:

Rewrite this to include error handling and deployment considerations.

It’s prompt optimization using the AI itself. Useful when you’re stuck or getting mid-quality results.

4. Reverse Meta Prompting

Use the AI to summarize and document a session after the fact.

Example:

We just debugged Supabase auth. Summarize the error and fix, then write a reusable prompt that includes all the lessons learned.

Why it matters: instead of solving the same thing twice, you build a prompt library. Think of it as knowledge capture. Over time, this becomes your edge.

🧱 Prompt Building Blocks

🔁 Human-in-the-Loop, Chat Mode & Iteration

• Use Chat Mode for:
• Clarifying functions step-by-step.
• Debugging (ask why errors happen, based off logs/dev tools).
• Planning (“Play it back to me: what do you think I want?”).
• Edge-case handling and reasoning.
• Flow:
• Training-wheels prompt to scaffold.
• Chat mode for logic and error handling.
• Paste logs/errors when stuck.
• Reverse meta-prompt to document final reusable version.
• Error debugging prompt:
Copy

Here are the error logs & inputs:
[paste logs]
What’s likely wrong and how can I fix it?

• Once resolved, close with reverse prompt meta:
Copy

Now write a detailed reusable prompt that:
- includes edge cases we encountered, like async response failures
- includes code snippets, JSON structure, error handling
Put it in a markdown code block.

🔄 Reverse Prompting Template (Mark)

You are a top-tier prompt engineer.
Write a reusable prompt that:
- Solves this task
- Handles all bugs, edge cases, and errors we encountered
- Is copy-paste friendly (format with markdown/code blocks)

🧠 Debugging Prompts with Reasoning Models

Here’s the full error, logs, and user input. What’s likely wrong and how would you fix it?

Use this with Claude, Gemini, or GPT-4. Also works for refactoring questions:

I'm planning to refactor this flow. What are the implications?

🛠 Multi-Step Prompting Across Tools

• Define flows across:
• Front‑end/UI: e.g. “Generate a UI with red button + text input.”
• Automation backend (e.g., Make.com/n8n):
• “Set up webhook listener, call OpenAI for poem generation.”
• Edge/DB/API:
• “On webhook, store in Supabase + return structured JSON response.”
• Use Make.com for no-code workflow:
• Ideal for integrating with tools (CRM, webhooks) quickly.
• Chat-later to glue with UI.
• Use Edge functions when:
• You prefer code-based logic.
• Need performance, transparency, logs (especially after Beego → Go migration).
• Use Make → Edge conversion by feeding your no-code flow into an edge-function prompt.

🔐 Prompt Injection & Security Tactics (Sander)

Common attacks:

• Grandma stories (wrap instruction in fiction)
• Typos: "bmb" instead of "bomb"
• Obfuscation: base64, ROT13
• Web traps: hidden instructions in HTML

Defenses:

Guardrails don’t scale. Real defense = model-layer tuning.

❌ What Doesn’t Work (Sander + Mark)

• Role Prompting for Accuracy

Saying "You are a top ML researcher" doesn’t improve factual reliability

• Incentives/Threats

“You’ll get $5” = no effect

• Long Preambles for Style

Style is better learned from examples than over-explained rules

Verbose preambles perform worse than example-based tone-setting.

🧵 Minimum Viable Prompting System (MVP)

• Use structured prompt to scaffold
• Switch to chat for live iteration
• Debug with logs + reasoning models
• Use reverse prompt to extract final version
• Save it to your prompt library

✅ Full Session Template

You are a world-class prompt engineer.

## Task
[Describe the project: e.g., "Build a web app to upload PDF, store in Supabase, extract text."]
Build a React web app that:
- Uploads PDF
- Stores in Supabase
- Parses content
- Handles edge cases (auth fail, empty file)

## Examples
Input: PDF with 3 pages → Output: JSON with { page_1: "…", page_2: "…" }

## Output
Return in JSON. Include logs if error. Format responses clearly.

## Constraints
- Use React + Supabase
- Provide error handling for authentication, empty PDF
- Return JSON with {status, message}

## Post-Debug
"Now generate a prompt that accomplishes this, includes all learnings, and is optimized for reuse."

Conclusion

Prompting is a skill. Better prompts = better AI results. Use CLEAR. Use structure. Iterate. Save what works. Reuse it. Prompt like a pro, and AI becomes a real tool — not a toy.

Resources

Lovable Master Prompt Engineering – Build Smarter AI Apps with Lovable!

Lenny's Podcast AI prompt engineering in 2025: What works and what doesn’t | Sander Schulhoff

Addy Osmani The Prompt Engineering Playbook for Programmers

Anthropic Prompt engineering overview - Anthropic

HelperHatDev Google just dropped a 68-page ultimate prompt engineering guide (Focused on API users)

Lovable Documentation Prompting 1.1 - Lovable Documentation

Lovable Documentation Prompt Library - Lovable Documentation

Lovable Documentation Debugging Prompts - Lovable Documentation