english/.opencode/skills/ai-artist/references/llm-prompting.md

LLM Prompting Reference

Prompt Architecture

System Prompt Structure

You are [ROLE] with expertise in [DOMAIN].

## Context
[Background, constraints, tone]

## Instructions
[Step-by-step task breakdown]

## Output Format
[Exact structure with example]

## Constraints
- [Hard limits]
- [Guardrails]

User Prompt Structure

<context>[Background information]</context>
<task>[Specific action required]</task>
<format>[Output structure]</format>
<constraints>[Additional limits]</constraints>

Reasoning Techniques

Chain of Thought (CoT)

Variant	Trigger	Best For
Zero-shot	"Think step by step"	Quick reasoning tasks
Few-shot	2-3 reasoning examples	Complex multi-step
Auto-CoT	"Let's approach systematically"	General reasoning

Tree of Thoughts (ToT)

Explore 3 approaches to [problem]:
For each: 1) Method 2) Pros/cons 3) Success probability
Evaluate branches, select best path.

Self-Consistency

Run same prompt 3-5x with temp=0.7, take majority answer. Best for: math, logic, factual.

ReAct Pattern

Thought: [Current reasoning]
Action: [Tool/step to take]
Observation: [Result]
...repeat...
Final Answer: [Conclusion]

Least-to-Most

Break [complex task] into subproblems.
Solve easiest first, build up.

Instruction Optimization

Self-Refine Pattern

1. Generate initial response
2. Critique: "What's wrong with this?"
3. Refine: "Fix identified issues"
4. Repeat until satisfactory

Role Optimization

• Expert persona: "As a senior [role] with 20 years..."
• Constraint persona: "You only respond with..."
• Teaching persona: "Explain as if to a..."

Task Decomposition

<subtasks>
1. [First step - output X]
2. [Second step - using X, output Y]
3. [Final step - using Y, output Z]
</subtasks>

Output Control

JSON Enforcement

Respond in valid JSON only:
{"field": "type", "required": true}
No markdown, no explanation, just JSON.

Length Control

Goal	Phrase
Brief	"In 2-3 sentences"
Detailed	"Comprehensive analysis in 500 words"
Structured	"5 bullet points, max 20 words each"

Hallucination Reduction

• "Only use information from provided context"
• "If unsure, say 'I don't know'"
• "Cite sources for each claim"
• "Confidence: high/medium/low for each point"

Model-Specific Tips

Claude

• XML tags: <thinking>, <answer>, <context>
• Extended thinking: "Think deeply before responding"
• Prefill: Start assistant response to guide format

GPT-4

• JSON mode: response_format: {"type": "json_object"}
• Function calling for structured output
• System message for persistent instructions

Gemini

• Multimodal: Image + text in same prompt
• Grounding: Enable Google Search for facts
• Safety settings: Adjust thresholds

Context Engineering

RAG Prompt Pattern

<retrieved_context>
[Document chunks with sources]
</retrieved_context>

Answer based ONLY on context above.
If not in context, say "Not found in documents."

Window Optimization

• Front-load critical info (primacy effect)
• Repeat key constraints at end (recency effect)
• Chunk long documents with summaries

Few-Shot Examples

Structure

Example 1:
Input: [representative input]
Output: [ideal output]

Example 2:
Input: [edge case]
Output: [handling]

Now apply to:
Input: [actual task]

Selection Criteria

• Diverse examples > similar examples
• Include edge cases
• Match complexity of target task
• 2-5 examples optimal (diminishing returns beyond)