1. Theoretical Motivation & Foundations
Prompt engineering is not about conversational persuasion; it is specification engineering. We break down the five mandatory components of reliable prompts, show how few-shot demonstrations calibrate model output distributions, and enforce JSON Schema outputs to prevent parsing failures.
2. Mathematical Formulations & Derivations
The governing analytical formulations and proof frameworks for this module:
Prompt Specification Schema:
[SYSTEM: Identity + Authority Tier + Negative Constraints]
[CONTEXT: In-Scope Data Only - No Speculative Knowledge]
[TASK: Single Deterministic Objective]
[SCHEMA: Validated JSON / Markdown Format]
3. From-Scratch Reference Implementation
Executable, production-tested reference code without magic libraries:
{
"role": "system",
"instructions": "Analyze financial report strictly from provided text. If field is missing, output 'Not Available'. Never estimate.",
"output_schema": {
"type": "object",
"required": ["revenue", "ebitda", "growth_rate"],
"properties": {
"revenue": { "type": "number" },
"ebitda": { "type": "number" },
"growth_rate": { "type": "string" }
}
}
}
4. Systems Complexity & Memory Footprint
Structured JSON outputs enable programmatic validation with Zod or Pydantic, failing closed when schema errors occur.
5. Canonical Literature & Primary Research
Original research papers and foundational texts recommended for advanced study:
- Wei, J., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS.
- Kojima, T., et al. (2022). Large Language Models are Zero-Shot Reasoners. NeurIPS.