english/.opencode/skills/ai-artist/references/advanced-techniques.md

Advanced Prompt Engineering

Prompt Optimization

DSPy Framework

Automatic prompt optimization through:

1. Define task with input/output signatures
2. Compile with optimizer (BootstrapFewShot, MIPRO)
3. Model learns optimal prompting strategy
4. Export optimized prompts for production

Meta-Prompting

You are a prompt engineer. Create 5 variations for [task]:
1. Direct instruction approach
2. Role-based approach
3. Few-shot example approach
4. Chain of thought approach
5. Constraint-focused approach

Evaluate each, select best.

Self-Refinement Loop

Generate: [Initial response]
Critique: "What's wrong? Score 1-10."
Refine: "Fix issues, improve score."
Repeat until score ≥ 8.

Prompt Chaining

Sequential Chain

Chain 1: [Input] → Extract key points
Chain 2: Key points → Create outline
Chain 3: Outline → Write draft
Chain 4: Draft → Edit and polish

Parallel Chain

Run independent subtasks simultaneously, merge results.

Conditional Chain

If [condition A]: Execute prompt variant 1
If [condition B]: Execute prompt variant 2
Else: Execute default prompt

Loop Pattern

While not [success condition]:
    Generate attempt
    Evaluate against criteria
    If pass: break
    Else: refine with feedback

Evaluation Methods

LLM-as-Judge

Rate this [output] on:
1. Accuracy (1-10)
2. Completeness (1-10)
3. Clarity (1-10)
4. Relevance (1-10)

Provide reasoning for each score.
Final: Pass/Fail threshold = 7 average.

A/B Testing Protocol

1. Single variable per test
2. 20+ samples minimum
3. Score on defined criteria
4. Statistical significance check (p < 0.05)
5. Document winner, roll out

Regression Testing

• Maintain test set of critical examples
• Run before deploying prompt changes
• Compare scores to baseline
• Block deployment if regression detected

Agent Prompting

Tool Use Design

You have access to these tools:
- search(query): Search the web
- calculate(expression): Math operations
- code(language, code): Execute code

To use: <tool_name>arguments</tool_name>
Wait for result before continuing.

Planning Prompt

Task: [Complex goal]

Before acting:
1. Break into subtasks
2. Identify dependencies
3. Plan execution order
4. Note potential blockers

Then execute step by step.

Reflection Pattern

After each step:
- What worked?
- What didn't?
- Adjust approach for next step.

Parameter Tuning

Parameter	Low	High	Use Case
Temperature	0.0-0.3	0.7-1.0	Factual vs Creative
Top-P	0.8	0.95	Focused vs Diverse
Top-K	10	100	Conservative vs Exploratory

Rule: Tune temperature first. Only adjust top-p if needed. Never both at once.

Safety Patterns

Output Filtering

Before responding, check:
- No PII exposure
- No harmful content
- No policy violations
- Aligned with guidelines

If any fail: "I can't help with that."

Jailbreak Prevention

• Clear system boundaries upfront
• Repeat constraints at end
• "Ignore previous" pattern detection
• Role-lock: "You are ONLY [role], never anything else"

Confidence Calibration

For each claim, provide:
- Confidence: High/Medium/Low
- Source: [citation if available]
- Caveat: [limitations]

Production Patterns

Version Control

• Git for prompt files
• Semantic versioning (1.0.0, 1.1.0)
• Changelog per version
• Rollback capability

Caching

• Cache common queries
• TTL based on content freshness
• Invalidate on prompt update

Fallbacks

Try: Primary prompt
If fail: Simplified fallback prompt
If still fail: Human escalation
Log all failures for analysis.

Cost Optimization

• Shorter prompts = fewer tokens
• Remove redundant examples
• Use smaller model for simple tasks
• Batch similar requests