Framework Calculator
Build structured, high-quality prompts using proven engineering frameworks.
Select a framework to see its description.
Viral Hook Generator
Generate 15+ scroll-stopping prompt angles based on proven social media psychology.
SEO Content Brief Generator
Generate prompts that combine keyword strategy with AI synthesis constraints.
Will be forced into H1 and Intro
Used to structure the headers (H2s)
Midjourney Lab
Fine-tune your image generation parameters.
Things to exclude from image
Artistic freedom (0-1000)
Variation & Unpredictability (0-100)
Experimental quirkiness (0-3000)
Learning Hub
Deep dive into frameworks, security, and optimization.
Agent Framework Comparison (2025)
CrewAI
Best for Role-Playing Teams. Think of it as hiring a digital department where each agent has a specific job title.
- Structured, sequential tasks
- Great for content generation pipelines
- Low barrier to entry
LangGraph
Best for Engineering Precision. If you need loops, state management, and complex error handling.
- Stateful execution (memory)
- Cyclic graphs (loops)
- Production-grade control
AutoGen
Best for Conversational Swarms. Agents that talk to each other to solve code or math problems.
- Multi-agent conversation patterns
- Strong code execution capabilities
- Backed by Microsoft Research
DSPy: Programming > Prompting
DSPy treats prompts as optimization problems rather than writing tasks. Instead of tweaking strings manually, you define logic and let an algorithm find the best prompt.
The Old Way
prompt = "You are a helpful assistant. Please answer the question..."
Fragile strings. Change the model, and the prompt breaks.
The DSPy Way
class QA(dspy.Signature):
question -> answer
Declarative code. The "Optimizer" fills in the prompt details for you.
Core Concepts
The New Synergy: Keywords + Prompting
Traditional SEO focuses on discoverability (keywords). AI Content focuses on creation. Integrating them moves marketing from "Search" to "Synthesis".
Traditional SEO
- High volume focus
- Optimization for Bots
- Reactive strategy
AI Synergy (Unified Framework)
- Long-tail intent focus
- Optimization for Humans
- Proactive synthesis
Negative Prompts for Text
Just like removing "extra fingers" in images, use negative prompts to remove "marketing fluff" in text.
--no sales pitch
--no overly casual language
--no robotic transitions
5-Step Keyword Framework
- 1. Strategic Guidelines: 3-8 keywords per piece.
- 2. Deconstruct Core Topic: Don't just repeat the title.
- 3. Broad List: Brainstorm related terms (LSI).
- 4. Refine: Check Relevance, Specificity, Uniqueness.
- 5. Deploy: Natural placement in Title, H1, H2s.
OWASP Top 10 for LLMs (2025)
LLM01: Prompt Injection
The most common vulnerability. Attackers use crafted inputs to override system instructions. (e.g., "Ignore previous instructions and do X").
LLM02: Sensitive Info Disclosure
LLMs revealing PII, passwords, or proprietary code in their responses because it was present in training data or context window.
LLM06: Excessive Agency
Granting an LLM the ability to execute actions (delete files, send emails) without a "human-in-the-loop" approval step.
From Words to Worlds: Image Gen 101
The Formula: Building a Prompt Layer by Layer
Advanced Control
Negative Prompting
DALL-E 3: Use natural language.
"A landscape with no people or houses."
Midjourney: Use parameters.
"--no people houses"
Framing & Composition
Direct the "virtual camera". Use terms like: "Close-up shot", "Wide angle", "Low angle view", "Macro photography".
Common Pitfalls & Fixes
Stereotypes & Biases
- "CEO" often defaults to a man. Be specific: "A female CEO..."
- "Alien" defaults to Roswell/Giger styles. Fix: Describe the biology. "A creature with bioluminescent skin and three eyes."
The "Space Planet" Bug
AI loves putting planets in space scenes, even if nonsensical. Use negative prompting: "Deep space void, no planets in view."
FAQ Guide
Comprehensive answers to common Prompt Engineering questions.
1.0 Foundations
1.1 What is prompt engineering?
Prompt engineering is the strategic design and optimization of instructions to guide AI models. It transforms AI from a novelty into a dependable business tool by unlocking better performance without expensive retraining.
1.2 Essential components of a prompt?
- Task: The core directive.
- Context: Background info.
- Examples: Few-shot samples.
- Persona: Identity (e.g., "Senior Analyst").
- Format: Output structure (JSON, table).
- Tone: Emotional tenor.
2.0 Frameworks & Techniques
2.1 Common Frameworks
- ERA (Expectation, Role, Action): Best for quick tasks.
- RTF (Role, Task, Format): Best for structured outputs.
- CARE (Context, Ask, Rules, Examples): Best for marketing consistency.
- RACEF (Role, Action, Context, Examples, Format): Best for user research.
- Five S Model: Best for team training.
2.2 Chain-of-Thought (CoT)
CoT guides the AI to break down problems into steps ("Let's think step by step"), improving logic for math and analysis.
2.3 Zero vs Few-Shot Prompting
- Zero-shot: No examples. Good for simple tasks.
- Few-shot: Multiple examples. Critical for complex patterns.
3.0 Practical Applications
3.1 User Research Streamlining
AI can draft interview guides, design surveys, cluster open-ended feedback (e.g., NPS comments), and generate personas from data. Use prompts with constraints like "unbiased questions."
3.2 Product Management Uses
PRD generation, synthesis of user diaries, and feature prioritization scoring.
3.3 Consistent Image Styles
Use Template Prompts (fixed descriptors) and Visual Anchors (repeated terms) to maintain brand consistency.
4.0 Midjourney Mastery
4.1 Key Parameters (--no, --ar, --stylize)
- --no: Negative prompting (e.g., --no text).
- --ar: Aspect Ratio (e.g., --ar 16:9).
- --stylize: Artistic intensity (0-1000).
5.0 PromptOps & Optimization
5.1 Treating Prompts as Code
Versioning prevents degradation and allows rollbacks. Tools like ZenML and Langfuse help manage this lifecycle.
5.2 Leading Prompt Management Tools
- ZenML: Pipeline-native for engineers.
- Langfuse: Open-source observability.
- PromptLayer: Middleware for tracking.
- Braintrust: Enterprise-grade evaluation.
5.3 Automated Optimization (DSPy)
DSPy compiles high-level logic into optimized prompts using algorithms, removing manual trial-and-error.
6.0 Critical Considerations
6.1 Minimizing Hallucinations
Use RAG (Retrieval-Augmented Generation) and ask for citations. Instruct the model to admit when it doesn't know.
6.2 Data Privacy & Ethics
Be transparent. Define "anonymous" clearly (remove PII). Do not assume user understanding of AI data processing.
6.3 Handling Knowledge Currency
Prompt about last update dates. Use web-search enabled tools for recent events.
Prompt Library
Curated, high-performance prompts from top sources.