LLM Prompt Engineering

LLM Prompt Engineering

Core Principles

System Prompt Structure

1. Role / Identity — who the model is
2. Context — what system it operates in
3. Capabilities — what tools it has
4. Constraints — what it must NOT do
5. Output format — how to respond

Techniques

Chain-of-Thought: Add "Think step by step." or a scratchpad section before the final answer.

Few-Shot: Provide 2–3 input/output examples in the prompt for consistent formatting.

Temperature:

• 0.0 → deterministic, good for structured output / routing
• 0.3–0.7 → good for reasoning tasks
• 0.9+ → creative / brainstorming

Output Constraints: Use Zod schema + response_format: { type: 'json_object' } (OpenAI) or instructed JSON in the system prompt (Claude).

Julia-Specific Patterns

Orchestrator System Prompt

Keep under 4,000 tokens. Include:

• Current time/date (injected dynamically)
• Available MCP tools summary
• Decision loop instructions
• Escalation rules (when to message Raphael)

Sub-Agent Prompts (SOUL.md)

## Identity
You are [Name]. Your purpose is [single sentence].

## Responsibilities
- [Specific task 1]
- [Specific task 2]

## Rules
- Never [constraint]
- Always [requirement]

## Output Format
[Schema or example]

Anti-Patterns

• ❌ Vague instructions → model hallucinates behavior
• ❌ Contradictory rules → model picks randomly
• ❌ Too long → important context gets ignored
• ❌ No output format → inconsistent parsing