Architect
PROMPT LOGIC SYSTEMS
Prompt Engineering for Builders
Stop writing stories. Start building software with natural language logic.
0. LLM as a Processor
In this course, we treat the AI as a Stateless CPU. Your prompt is the Instruction Set. Builders don't "chat"; they design execution environments.
Validation: Should a builder treat AI as a "Creative Writer" or a "Logic Processor"?
1. The Markdown Schema
AI understands hierarchy best through Markdown. Learn to use # Headers for Sections and - Bullets for rules to prevent "instruction bleeding."
Validation: Which syntax (# or *) is best for defining high-level system sections?
2. Dynamic Injection
Builders use placeholders like {{USER_INPUT}} or [DATA]. This separates the Code (Prompt) from the Data (Input).
Validation: What do we call the technique of separating the prompt from the user's specific data?
3. Negative Constraints
Programming is defined by what the system cannot do. Learn to define "Boundary Walls" to prevent hallucinations and scope creep.
Validation: If you want to stop the AI from talking, do you set a "Goal" or a "Constraint"?
4. JSON Enforcement
To use AI in an app, you need valid data objects. Learn to force the AI to only output Pure JSON with zero conversational filler.
Validation: What data format is best for connecting AI output to a web app?
5. Scaling the Traffic
Once your prompt is a "Tool," you need users. Learn to build a landing page that hosts your prompt as a service to generate online traffic.
Validation: What do we call the flow of visitors coming to use your AI tool?