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+# 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