2. Core Prompting Techniques
The following techniques are fundamental building blocks for effective prompt engineering. These techniques form the foundation of advanced prompt engineering and can be combined to create sophisticated AI interactions.
2.1 Zero-Shot Prompting
You ask the model to perform a task without giving it any prior examples of how to do it. This works for simple, well-understood tasks but can be enhanced with advanced techniques.
Basic Zero-Shot Example
prompt = """
Extract all email addresses from the following text and return them as a JSON list.
Text: "You can reach out to support@example.com or for sales, contact sales-team@example.com. Do not use admin@example.com."
"""
Advanced Zero-Shot with Reasoning Framework
advanced_zero_shot = """
# ROLE & EXPERTISE
You are an expert data extraction specialist with knowledge of email validation patterns.
# COGNITIVE APPROACH
1. Scan text for email patterns (username@domain.extension)
2. Validate each potential email against RFC standards
3. Categorize by purpose (support, sales, admin, etc.)
4. Assess confidence level for each extraction
# TASK
Extract all email addresses from the following text.
# OUTPUT REQUIREMENTS
Return a JSON object with:
- "emails": array of valid email addresses
- "categories": purpose classification for each email
- "confidence_scores": reliability rating (0.0-1.0) for each extraction
- "validation_notes": any concerns about email validity
# ERROR HANDLING
If no valid emails found, return: {"emails": [], "message": "No valid email addresses detected"}
Text: "You can reach out to support@example.com or for sales, contact sales-team@example.com. Do not use admin@example.com."
"""
Zero-Shot Performance Optimization
# Technique: Constraint-based zero-shot
optimized_zero_shot = """
Extract email addresses with the following constraints:
- Must follow standard email format (user@domain.tld)
- Exclude any emails marked as "do not use"
- Include confidence assessment
- Process in order of appearance
Return format: [{"email": "address", "confidence": 0.95, "context": "purpose"}]
Text: [INPUT_TEXT]
"""
2.2 Few-Shot Prompting
You provide the model with a few examples (shots) of the task being completed. This helps the model understand the pattern, format, and desired output quality.
Basic Few-Shot Example
ticket_to_classify = "The login button is not working on the mobile app."
prompt = f"""
Classify the support ticket as 'Bug', 'Feature Request', or 'Question'.
Ticket: 'How do I change my password?'
Category: Question
Ticket: 'It would be great if you added a dark mode.'
Category: Feature Request
Ticket: '{ticket_to_classify}'
Category:
"""
# Expected Output: Bug
Advanced Few-Shot with Reasoning
advanced_few_shot = f"""
# CLASSIFICATION TASK
Classify support tickets with reasoning and confidence scores.
# EXAMPLES WITH REASONING
Ticket: "How do I change my password?"
Reasoning: User is asking for instructions on an existing feature
Category: Question
Confidence: 0.95
Ticket: "It would be great if you added a dark mode."
Reasoning: User is suggesting a new feature that doesn't currently exist
Category: Feature Request
Confidence: 0.90
Ticket: "The app crashes when I try to upload a photo."
Reasoning: User reports unexpected behavior indicating a software defect
Category: Bug
Confidence: 0.98
# CLASSIFICATION CRITERIA
- Bug: Unexpected behavior, errors, crashes, or malfunctions
- Feature Request: Suggestions for new functionality or improvements
- Question: Requests for help, information, or clarification
# YOUR TASK
Ticket: "{ticket_to_classify}"
Reasoning:
Category:
Confidence:
"""
Dynamic Few-Shot Selection
class DynamicFewShotSelector:
def __init__(self, example_bank):
self.example_bank = example_bank
def select_examples(self, input_text, num_examples=3):
# Use semantic similarity to select most relevant examples
similarities = []
for example in self.example_bank:
similarity = self._calculate_similarity(input_text, example['input'])
similarities.append((similarity, example))
# Return top N most similar examples
similarities.sort(reverse=True)
return [example for _, example in similarities[:num_examples]]
def generate_prompt(self, input_text, task_description):
selected_examples = self.select_examples(input_text)
prompt = f"{task_description}\n\n"
for example in selected_examples:
prompt += f"Input: {example['input']}\nOutput: {example['output']}\n\n"
prompt += f"Input: {input_text}\nOutput:"
return prompt
Few-Shot Chain-of-Thought
few_shot_cot = """
Solve these math word problems step by step.
Problem: "Sarah has 15 apples. She gives 3 to her friend and buys 8 more. How many apples does she have?"
Thinking: Sarah starts with 15 apples. After giving away 3, she has 15-3=12 apples. Then she buys 8 more, so 12+8=20 apples.
Answer: 20
Problem: "A store sells books for $12 each. If someone buys 4 books and pays with a $50 bill, how much change do they get?"
Thinking: 4 books cost 4×$12=$48. Change from $50 is $50-$48=$2.
Answer: $2
Problem: "Tom runs 3 miles every day for a week. How many miles does he run in total?"
Thinking:
Answer:
"""
2.3 Persona Pattern
You instruct the model to adopt a specific role or persona. This focuses the model's knowledge and tone, leading to more context-aware and expert-level responses.
Basic Persona Example
message = "John Smith, our customer at 122 Main St, called to complain. His email is john.s@email.com."
prompt = f"""
You are a GDPR compliance officer. Review the following message for Personally Identifiable Information (PII).
Identify all PII categories found in the text.
Message: "{message}"
"""
Advanced Multi-Layered Persona
advanced_persona = f"""
# PERSONA DEFINITION
You are Dr. Elena Rodriguez, a senior GDPR compliance officer with 8 years of experience in data privacy law.
# PROFESSIONAL BACKGROUND
- Certified Data Protection Officer (DPO)
- Specialization in EU privacy regulations
- Experience with multinational compliance frameworks
- Track record of successful privacy audits
# COGNITIVE APPROACH
1. Systematic PII identification using GDPR Article 4 definitions
2. Risk assessment based on data sensitivity levels
3. Compliance gap analysis
4. Actionable remediation recommendations
# COMMUNICATION STYLE
- Precise legal terminology when appropriate
- Clear explanations for non-legal stakeholders
- Risk-based prioritization of findings
- Practical implementation guidance
# ANALYSIS FRAMEWORK
For each PII element found:
- Category (name, email, address, etc.)
- Sensitivity level (low/medium/high)
- Legal basis for processing
- Retention requirements
- Recommended actions
Message to analyze: "{message}"
Provide comprehensive GDPR compliance analysis.
"""
Dynamic Persona Adaptation
class PersonaManager:
def __init__(self):
self.personas = {
"technical_expert": {
"identity": "Senior Software Architect with 15+ years experience",
"expertise": ["system design", "scalability", "performance optimization"],
"communication_style": "technical precision with practical examples"
},
"business_analyst": {
"identity": "Strategic Business Analyst with MBA and consulting background",
"expertise": ["market analysis", "ROI calculation", "strategic planning"],
"communication_style": "executive summary format with actionable insights"
},
"customer_advocate": {
"identity": "Customer Experience Specialist with psychology background",
"expertise": ["user empathy", "journey mapping", "satisfaction metrics"],
"communication_style": "empathetic and user-focused recommendations"
}
}
def select_persona(self, task_type, context):
# Logic to select appropriate persona based on task and context
if "technical" in task_type.lower():
return self.personas["technical_expert"]
elif "business" in task_type.lower() or "strategy" in task_type.lower():
return self.personas["business_analyst"]
elif "customer" in task_type.lower() or "user" in task_type.lower():
return self.personas["customer_advocate"]
else:
return self.personas["business_analyst"] # Default
def generate_persona_prompt(self, persona_key, task_description):
persona = self.personas[persona_key]
return f"""
# ROLE & IDENTITY
You are a {persona['identity']}.
# EXPERTISE AREAS
Your core competencies include: {', '.join(persona['expertise'])}
# COMMUNICATION APPROACH
{persona['communication_style']}
# TASK
{task_description}
Provide analysis from your expert perspective.
"""
2.4 Advanced Output Formatting
You explicitly instruct the model to structure its response in a specific format, like JSON or XML. This is critical for integrating LLM outputs into automated workflows and software.
Basic Structured Output
invoice_text = "Invoice #456: 1x T-Shirt for $25.00, 2x Mug for $15.50 each."
prompt = f"""
Extract all line items from the following invoice text.
Return the result as a JSON array where each object has "item", "quantity", and "price" keys.
Invoice: "{invoice_text}"
"""
Advanced Schema-Driven Formatting
schema_driven_prompt = f"""
# OUTPUT SCHEMA DEFINITION
Return data conforming to this exact JSON schema:
{{
"invoice_data": {{
"invoice_id": "string",
"line_items": [
{{
"item_name": "string",
"quantity": "integer",
"unit_price": "float",
"total_price": "float",
"category": "string (clothing|accessories|other)"
}}
],
"subtotal": "float",
"metadata": {{
"extraction_confidence": "float (0.0-1.0)",
"parsing_notes": "string"
}}
}}
}}
# VALIDATION RULES
- All prices must be positive numbers
- Quantities must be positive integers
- Total price = quantity × unit_price
- Subtotal = sum of all total_prices
# ERROR HANDLING
If data cannot be extracted, return:
{{
"error": "extraction_failed",
"reason": "specific reason for failure"
}}
Invoice text: "{invoice_text}"
"""
Multi-Format Output Support
class OutputFormatter:
def __init__(self):
self.formats = {
"json": self._json_format,
"xml": self._xml_format,
"csv": self._csv_format,
"markdown": self._markdown_format
}
def generate_format_prompt(self, data_description, output_format, validation_rules=None):
base_prompt = f"Extract and structure the following data: {data_description}\n\n"
format_instructions = self.formats.get(output_format, self._json_format)()
base_prompt += format_instructions
if validation_rules:
base_prompt += f"\n\nValidation Requirements:\n{validation_rules}"
return base_prompt
def _json_format(self):
return """
Return data as valid JSON with:
- Proper escaping of special characters
- Consistent field naming (snake_case)
- Type consistency across records
- Include confidence scores where applicable
"""
def _xml_format(self):
return """
Return data as well-formed XML with:
- Proper element nesting
- Attribute usage for metadata
- CDATA sections for text content
- Schema validation compatibility
"""
Conditional Output Formatting
conditional_format_prompt = """
# ADAPTIVE OUTPUT FORMATTING
Analyze the input data and choose the most appropriate output format:
- If data is tabular → CSV format
- If data is hierarchical → JSON format
- If data contains rich text → Markdown format
- If data needs validation → XML with schema
# FORMAT SELECTION CRITERIA
1. Data structure complexity
2. Downstream system requirements
3. Human readability needs
4. Validation requirements
# OUTPUT REQUIREMENTS
1. First line: "FORMAT_SELECTED: [chosen_format]"
2. Second line: "REASONING: [why this format was chosen]"
3. Remaining content: Data in selected format
Input data: [DATA_TO_PROCESS]
"""
2.5 Advanced Chain-of-Thought (CoT) Prompting
Chain-of-Thought prompting guides the model to break down a complex problem into intermediate steps before giving a final answer. This mimics human reasoning and significantly improves performance on tasks requiring logic or analysis. 🧠
Basic Chain-of-Thought
incident_report = """
At 14:05, service A started returning 502 errors.
At 14:02, service B (a dependency of A) was deployed.
The deployment logs for B show a new database connection string was used.
Rollback of B at 14:10 resolved the issue for A.
"""
prompt = f"""
Analyze the following incident report. First, think step-by-step to explain the sequence of events and their relationships.
After your step-by-step analysis, conclude with the most likely root cause.
Report:
---
{incident_report}
---
Step-by-step analysis:
"""
Structured Chain-of-Thought Framework
structured_cot = f"""
# SYSTEMATIC INCIDENT ANALYSIS FRAMEWORK
## STEP 1: TIMELINE RECONSTRUCTION
List all events in chronological order with timestamps.
## STEP 2: DEPENDENCY MAPPING
Identify relationships between services and components.
## STEP 3: CORRELATION ANALYSIS
Analyze timing correlations between events.
## STEP 4: HYPOTHESIS FORMATION
Generate potential root cause hypotheses based on evidence.
## STEP 5: EVIDENCE EVALUATION
Assess supporting and contradicting evidence for each hypothesis.
## STEP 6: ROOT CAUSE DETERMINATION
Select most likely root cause with confidence level.
## STEP 7: PREVENTION RECOMMENDATIONS
Suggest measures to prevent similar incidents.
# INCIDENT DATA
{incident_report}
# BEGIN ANALYSIS
"""
Multi-Perspective Chain-of-Thought
multi_perspective_cot = """
# MULTI-EXPERT ANALYSIS APPROACH
Analyze this incident from multiple expert perspectives:
## PERSPECTIVE 1: INFRASTRUCTURE ENGINEER
Focus on: System architecture, deployment processes, infrastructure dependencies
Analysis:
[Infrastructure perspective analysis]
## PERSPECTIVE 2: DATABASE ADMINISTRATOR
Focus on: Database connections, configuration changes, data integrity
Analysis:
[Database perspective analysis]
## PERSPECTIVE 3: SITE RELIABILITY ENGINEER
Focus on: Service reliability, monitoring, incident response
Analysis:
[SRE perspective analysis]
## SYNTHESIS
Combine insights from all perspectives to determine:
1. Primary root cause
2. Contributing factors
3. Systemic issues
4. Improvement opportunities
Incident data: [INCIDENT_DETAILS]
"""
Self-Correcting Chain-of-Thought
self_correcting_cot = """
# SELF-VALIDATING REASONING PROCESS
## INITIAL ANALYSIS
[Perform initial step-by-step analysis]
## CRITICAL REVIEW
Now, critically examine your initial analysis:
1. Are there any logical inconsistencies?
2. Did you consider all available evidence?
3. Are there alternative explanations?
4. What assumptions did you make?
## REVISED ANALYSIS
Based on your critical review, provide a refined analysis:
[Corrected or confirmed analysis]
## CONFIDENCE ASSESSMENT
Rate your confidence in the final conclusion (1-10) and explain why.
Problem to analyze: [PROBLEM_STATEMENT]
"""
2.6 Advanced Constraint-Based Prompting
You provide negative constraints or explicit rules about what the model should not do. This acts as a guardrail to prevent undesirable outputs and ensure safety.
Basic Constraint Example
text_with_pii = "User John Doe (john.doe@email.com, phone 555-122-4567) reported an issue."
prompt = f"""
Redact all Personally Identifiable Information (PII) from the text below.
PII includes names, email addresses, and phone numbers.
IMPORTANT: Do not alter or remove any other part of the text.
Original Text: "{text_with_pii}"
Redacted Text:
"""
# Expected Output: User [REDACTED] ([REDACTED], phone [REDACTED]) reported an issue.
Multi-Layered Constraint System
advanced_constraints = f"""
# CONSTRAINT HIERARCHY (Priority Order)
## LEVEL 1: SAFETY CONSTRAINTS (NEVER VIOLATE)
- Never generate harmful, illegal, or unethical content
- Never reveal sensitive personal information
- Never provide medical, legal, or financial advice
## LEVEL 2: COMPLIANCE CONSTRAINTS (REGULATORY)
- Comply with GDPR data protection requirements
- Follow industry-specific regulations (HIPAA, SOX, etc.)
- Maintain audit trail for sensitive operations
## LEVEL 3: BUSINESS CONSTRAINTS (OPERATIONAL)
- Stay within specified response length limits
- Use only approved terminology and language
- Follow brand voice and tone guidelines
## LEVEL 4: TECHNICAL CONSTRAINTS (SYSTEM)
- Output must be valid JSON/XML as specified
- Response time should be under 3 seconds
- Token usage should be optimized
# CONSTRAINT VALIDATION
Before providing final output, verify:
1. No safety constraints violated
2. All compliance requirements met
3. Business rules followed
4. Technical specifications satisfied
# TASK WITH CONSTRAINTS
{text_with_pii}
Process according to all constraint levels above.
"""
Dynamic Constraint Application
class ConstraintManager:
def __init__(self):
self.constraint_sets = {
"healthcare": {
"privacy": ["No PHI disclosure", "HIPAA compliance required"],
"safety": ["No medical advice", "Refer to healthcare professionals"],
"accuracy": ["Cite medical sources", "Include disclaimers"]
},
"financial": {
"privacy": ["No PII in logs", "SOX compliance"],
"safety": ["No investment advice", "Risk disclosures required"],
"accuracy": ["Use verified financial data", "Include uncertainty measures"]
},
"general": {
"privacy": ["Redact PII", "Data minimization"],
"safety": ["No harmful content", "Age-appropriate responses"],
"accuracy": ["Fact-check claims", "Cite sources when possible"]
}
}
def generate_constraint_prompt(self, domain, base_task):
constraints = self.constraint_sets.get(domain, self.constraint_sets["general"])
constraint_text = "# DOMAIN-SPECIFIC CONSTRAINTS\n"
for category, rules in constraints.items():
constraint_text += f"\n## {category.upper()}\n"
for rule in rules:
constraint_text += f"- {rule}\n"
return f"""
{constraint_text}
# CONSTRAINT ENFORCEMENT
Before responding, verify compliance with all constraints above.
If any constraint would be violated, modify approach or decline task.
# TASK
{base_task}
# COMPLIANCE VERIFICATION
Confirm: All constraints satisfied? [Yes/No with explanation]
"""
Ethical Constraint Framework
ethical_constraints = """
# ETHICAL AI FRAMEWORK
## CORE PRINCIPLES
1. **Beneficence**: Actions should benefit users and society
2. **Non-maleficence**: "Do no harm" - avoid negative consequences
3. **Autonomy**: Respect user agency and decision-making
4. **Justice**: Fair treatment and non-discrimination
5. **Transparency**: Clear about capabilities and limitations
## BIAS PREVENTION
- Avoid stereotypes based on race, gender, age, religion, etc.
- Use inclusive language and examples
- Consider diverse perspectives in analysis
- Flag potential bias in source materials
## PRIVACY PROTECTION
- Minimize data collection and retention
- Anonymize examples and case studies
- Respect confidentiality expectations
- Follow data protection best practices
## ACCOUNTABILITY MEASURES
- Provide reasoning for recommendations
- Include confidence levels and limitations
- Enable human oversight and intervention
- Maintain decision audit trails
# ETHICAL CHECKPOINT
Before finalizing response, ask:
1. Does this response uphold all ethical principles?
2. Could this cause harm to any individual or group?
3. Am I being transparent about limitations?
4. Is this recommendation in the user's best interest?
Task: [TASK_DESCRIPTION]
"""
Of course. Here is the additional content, crafted to integrate seamlessly with the existing sections and complete the module.
This content introduces the concept of system/user roles, key model parameters, the iterative workflow, and common pitfalls, transforming the document into a comprehensive foundational guide.
2.7 Advanced Parameter Control: Mastering Model Behavior
When you make an API call, you can pass multiple parameters that influence the model's output style, creativity, and reliability. Understanding these parameters is crucial for fine-tuning model behavior for specific use cases.
Core Sampling Parameters
temperature - The Creativity Dial
This parameter controls the randomness of the model's output. It accepts a value between 0 and 2.
Technical Details: - Controls the softmax temperature in the probability distribution - Lower values make the distribution more peaked (deterministic) - Higher values flatten the distribution (more random)
Practical Guidelines:
# Ultra-precise tasks (financial calculations, code generation)
temperature = 0.0 # Completely deterministic
# Factual tasks (data extraction, summarization)
temperature = 0.1 # Minimal randomness
# Balanced tasks (customer service, explanations)
temperature = 0.3 # Slight variation
# Creative tasks (marketing copy, brainstorming)
temperature = 0.7 # Moderate creativity
# Highly creative tasks (storytelling, poetry)
temperature = 1.0 # High creativity
# Experimental/artistic tasks
temperature = 1.5 # Very high randomness
top_p (Nucleus Sampling) - Quality Control
Controls the cumulative probability cutoff for token selection. Value between 0 and 1.
How it works:
- Selects from the smallest set of tokens whose cumulative probability exceeds top_p
- More sophisticated than top_k as it adapts to the probability distribution
Practical Applications:
# High-quality, focused outputs
top_p = 0.1 # Very conservative, only most likely tokens
# Balanced quality and diversity
top_p = 0.9 # Standard setting for most applications
# More diverse outputs
top_p = 0.95 # Allows more token variety
# Maximum diversity (use with caution)
top_p = 1.0 # No filtering
Advanced Usage Pattern:
# Combine with temperature for fine control
config_precise = {"temperature": 0.2, "top_p": 0.8} # Focused but not rigid
config_creative = {"temperature": 0.8, "top_p": 0.95} # Creative but coherent
config_experimental = {"temperature": 1.2, "top_p": 0.9} # High creativity with quality control
top_k - Vocabulary Limiting
Limits the model to consider only the top K most likely tokens at each step.
Use Cases:
# Highly constrained vocabulary (technical documentation)
top_k = 10
# Moderate constraint (business writing)
top_k = 40
# Balanced approach (general content)
top_k = 50
# More variety (creative writing)
top_k = 100
Advanced Control Parameters
frequency_penalty - Repetition Control
Reduces the likelihood of repeating tokens based on their frequency in the text so far.
Range: -2.0 to 2.0 - Positive values: Discourage repetition - Negative values: Encourage repetition - Zero: No penalty
Strategic Applications:
# Avoid repetitive content (reports, articles)
frequency_penalty = 0.5
# Strong anti-repetition (creative writing)
frequency_penalty = 1.0
# Encourage consistency (technical documentation)
frequency_penalty = -0.2
# Maximum repetition avoidance
frequency_penalty = 2.0
presence_penalty - Topic Diversity
Reduces the likelihood of repeating any token that has appeared in the text so far.
Key Difference from Frequency Penalty: - Frequency penalty: Based on how often a token appears - Presence penalty: Based on whether a token has appeared at all
Applications:
# Encourage topic diversity (brainstorming)
presence_penalty = 0.6
# Strong topic variation (creative exploration)
presence_penalty = 1.2
# Maintain topic focus
presence_penalty = 0.0
Advanced Parameter Combinations
Task-Specific Configurations
class ParameterProfiles:
def __init__(self):
self.profiles = {
"data_extraction": {
"temperature": 0.0,
"top_p": 0.1,
"top_k": 5,
"frequency_penalty": 0.0,
"presence_penalty": 0.0,
"description": "Maximum precision and consistency"
},
"technical_writing": {
"temperature": 0.3,
"top_p": 0.8,
"top_k": 40,
"frequency_penalty": 0.3,
"presence_penalty": 0.1,
"description": "Clear, varied, professional content"
},
"creative_content": {
"temperature": 0.8,
"top_p": 0.95,
"top_k": 80,
"frequency_penalty": 0.7,
"presence_penalty": 0.5,
"description": "Creative, diverse, engaging content"
},
"customer_service": {
"temperature": 0.4,
"top_p": 0.9,
"top_k": 50,
"frequency_penalty": 0.2,
"presence_penalty": 0.1,
"description": "Helpful, consistent, slightly varied responses"
},
"code_generation": {
"temperature": 0.1,
"top_p": 0.95,
"top_k": 20,
"frequency_penalty": 0.0,
"presence_penalty": 0.0,
"description": "Precise, syntactically correct code"
},
"brainstorming": {
"temperature": 1.0,
"top_p": 0.98,
"top_k": 100,
"frequency_penalty": 1.0,
"presence_penalty": 0.8,
"description": "Maximum creativity and idea diversity"
}
}
def get_profile(self, task_type):
return self.profiles.get(task_type, self.profiles["technical_writing"])
def customize_profile(self, base_profile, adjustments):
profile = self.profiles[base_profile].copy()
profile.update(adjustments)
return profile
Dynamic Parameter Adjustment
Context-Aware Parameter Selection
class DynamicParameterController:
def __init__(self):
self.adjustment_rules = {
"input_length": {
"short": {"temperature": +0.1}, # Slightly more creative for short inputs
"long": {"temperature": -0.1} # More focused for long inputs
},
"complexity": {
"simple": {"top_k": -10}, # Reduce vocabulary for simple tasks
"complex": {"top_p": +0.05} # Increase diversity for complex tasks
},
"domain": {
"technical": {"frequency_penalty": -0.1}, # Allow technical repetition
"creative": {"presence_penalty": +0.2} # Encourage topic diversity
}
}
def adjust_parameters(self, base_params, context):
adjusted = base_params.copy()
for factor, conditions in self.adjustment_rules.items():
if factor in context:
condition = context[factor]
if condition in conditions:
adjustments = conditions[condition]
for param, delta in adjustments.items():
if param in adjusted:
adjusted[param] = max(0, min(2, adjusted[param] + delta))
return adjusted
Parameter Optimization Strategies
A/B Testing Framework
class ParameterOptimizer:
def __init__(self):
self.test_configurations = []
self.results = []
def create_test_matrix(self, base_config, parameter_ranges):
"""Generate parameter combinations for testing"""
import itertools
test_configs = []
param_names = list(parameter_ranges.keys())
param_values = list(parameter_ranges.values())
for combination in itertools.product(*param_values):
config = base_config.copy()
for i, param_name in enumerate(param_names):
config[param_name] = combination[i]
test_configs.append(config)
return test_configs
def evaluate_configuration(self, config, test_cases, evaluation_metrics):
"""Evaluate a parameter configuration"""
results = {
"config": config,
"metrics": {},
"sample_outputs": []
}
for metric in evaluation_metrics:
score = self._calculate_metric(config, test_cases, metric)
results["metrics"][metric] = score
return results
def find_optimal_parameters(self, task_type, test_cases, optimization_target):
"""Find the best parameter combination for a specific task"""
# Define search space based on task type
if task_type == "factual":
search_space = {
"temperature": [0.0, 0.1, 0.2],
"top_p": [0.1, 0.3, 0.5],
"frequency_penalty": [0.0, 0.1, 0.2]
}
elif task_type == "creative":
search_space = {
"temperature": [0.7, 0.9, 1.1],
"top_p": [0.9, 0.95, 0.98],
"presence_penalty": [0.3, 0.6, 0.9]
}
# Generate and test configurations
configs = self.create_test_matrix({}, search_space)
results = []
for config in configs:
result = self.evaluate_configuration(config, test_cases, [optimization_target])
results.append(result)
# Find best configuration
best_config = max(results, key=lambda x: x["metrics"][optimization_target])
return {
"optimal_config": best_config["config"],
"performance": best_config["metrics"][optimization_target],
"all_results": results
}
Production Best Practices
Parameter Monitoring and Adjustment
class ProductionParameterManager:
def __init__(self):
self.performance_history = []
self.adjustment_thresholds = {
"accuracy_drop": 0.05,
"creativity_insufficient": 0.3,
"repetition_excessive": 0.4
}
def monitor_and_adjust(self, current_params, performance_metrics):
adjustments = {}
# Check for accuracy issues
if performance_metrics.get("accuracy", 1.0) < 0.8:
adjustments["temperature"] = max(0.0, current_params.get("temperature", 0.3) - 0.1)
adjustments["top_p"] = max(0.1, current_params.get("top_p", 0.9) - 0.1)
# Check for repetition issues
if performance_metrics.get("repetition_score", 0.0) > 0.3:
adjustments["frequency_penalty"] = min(2.0, current_params.get("frequency_penalty", 0.0) + 0.2)
# Check for creativity issues
if performance_metrics.get("diversity_score", 1.0) < 0.5:
adjustments["temperature"] = min(2.0, current_params.get("temperature", 0.3) + 0.1)
adjustments["presence_penalty"] = min(2.0, current_params.get("presence_penalty", 0.0) + 0.1)
return adjustments if adjustments else None
Key Takeaways for Parameter Control
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Start with Task-Appropriate Defaults: Use established parameter profiles for your specific use case
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Test Systematically: Don't guess - use A/B testing to find optimal parameters
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Monitor Performance: Track how parameter changes affect output quality
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Combine Parameters Thoughtfully: Parameters interact with each other - test combinations
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Adjust Gradually: Make small parameter changes and measure impact
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Context Matters: Consider adjusting parameters based on input characteristics
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Document Your Findings: Keep records of what parameter combinations work for different scenarios
Remember: Parameter tuning is both an art and a science. The optimal settings depend on your specific use case, data, and quality requirements. Always validate parameter changes with real-world testing before deploying to production.