LLM Best Practices

This guide synthesizes best practices for working with LLMs across the development lifecycle, from prompt design to production deployment.

Prompt Engineering Best Practices

Clear Instructions

Best practices:

1. Be specific: "Summarize in 3-5 bullet points" vs. "Summarize"
2. Provide context: Include relevant background information
3. Specify format: Define output structure explicitly
4. Set constraints: Clarify boundaries and limitations

Structured Prompts

## Task
Summarize the provided research paper.

## Requirements
- Length: 150-200 words
- Focus: Key findings and methodology
- Audience: General technical audience
- Format: Paragraph with clear topic sentence

## Paper
[Insert paper content here]

## Summary

Few-Shot Examples

Best practices:

1. Diverse examples: Cover different cases
2. Representative examples: Match target distribution
3. Consistent formatting: Use identical structure
4. Appropriate count: 3-5 examples typically sufficient

Chain-of-Thought Prompting

Evaluation Best Practices

Multi-Dimensional Evaluation

Human Evaluation

Guidelines:

1. Clear rubrics: Define evaluation criteria precisely
2. Training: Calibrate evaluators with examples
3. Multiple evaluators: Use 3+ evaluators per sample
4. Inter-annotator agreement: Measure consistency
5. Regular calibration: Re-calibrate periodically

Automated Evaluation

Deployment Best Practices

Error Handling

import time
from functools import wraps

def retry_with_backoff(max_retries=3, backoff_factor=2):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except RateLimitError:
                    if attempt == max_retries - 1:
                        raise
                    time.sleep(backoff_factor ** attempt)
                except Exception as e:
                    if attempt == max_retries - 1:
                        raise
                    continue
        return wrapper
    return decorator

@retry_with_backoff(max_retries=3)
def generate_response(prompt):
    return llm.generate(prompt)

Rate Limiting

Implementation strategies:

1. Token bucket: Allow burst with sustained limit
2. Sliding window: Time-based request counting
3. User-based limits: Different limits per user tier
4. Endpoint-based limits: Different limits per endpoint

Caching

Caching strategies:

1. Exact match: Cache identical prompts
2. Semantic cache: Cache similar prompts
3. Prefix cache: Cache common prompt prefixes
4. Result cache: Cache generated results

Monitoring

Key metrics:

1. Latency: Response time percentiles
2. Throughput: Requests per second
3. Error rate: Failed requests percentage
4. Cost: Token usage and expenses
5. Quality: Output quality scores

Optimization Best Practices

Model Selection

Prompt Optimization

Optimization strategies:

1. Prompt compression: Reduce prompt length while maintaining quality
2. Template reuse: Standardize common prompt patterns
3. Few-shot optimization: Select optimal examples
4. Instruction tuning: Fine-tune for specific tasks

Quantization

Safety Best Practices

Input Validation

def validate_input(prompt: str) -> str:
    # Length check
    if len(prompt) > MAX_LENGTH:
        raise ValueError("Prompt too long")
    
    # Content filtering
    if contains_harmful_content(prompt):
        raise ValueError("Harmful content detected")
    
    # Injection detection
    if detect_injection(prompt):
        raise ValueError("Potential injection detected")
    
    return sanitize(prompt)

Output Filtering

Filtering layers:

1. Safety filter: Remove harmful content
2. Fact check: Verify factual claims
3. PII detection: Remove personal information
4. Quality filter: Remove low-quality outputs

Red Teaming

Red teaming checklist:

1. Safety: Attempt to generate harmful content
2. Bias: Test for discriminatory outputs
3. Robustness: Test with adversarial inputs
4. Privacy: Attempt to extract training data
5. Accuracy: Test factual correctness

Production Best Practices

Version Control

Version control components:

1. Model versions: Track model weights and architectures
2. Prompt versions: Version prompt templates
3. Data versions: Track training and evaluation data
4. Configuration versions: Version system configurations

A/B Testing

class ABTestManager:
    def __init__(self):
        self.traffic_split = 0.5  # 50/50 split
    
    def route_request(self, request):
        if random.random() < self.traffic_split:
            return self.model_a.generate(request)
        else:
            return self.model_b.generate(request)
    
    def analyze_results(self, results_a, results_b):
        # Compare metrics
        metric_a = self.calculate_metric(results_a)
        metric_b = self.calculate_metric(results_b)
        
        # Statistical significance test
        p_value = self.statistical_test(metric_a, metric_b)
        
        return {
            "winner": "A" if metric_a > metric_b else "B",
            "improvement": abs(metric_a - metric_b),
            "p_value": p_value
        }

Incident Response

Incident response steps:

1. Detection: Monitor for anomalies
2. Triage: Assess severity and impact
3. Mitigation: Apply immediate fixes
4. Resolution: Implement permanent solutions
5. Post-mortem: Analyze and prevent recurrence

Cost Optimization

Cost-saving strategies:

1. Caching: Reduce redundant generation
2. Batching: Process requests together
3. Quantization: Use efficient model formats
4. Model routing: Use appropriate model sizes
5. Prompt optimization: Reduce token usage

Best Practices Summary

Development Phase

1. Start simple: Begin with basic prompts before complex chains
2. Iterate rapidly: Test and refine quickly
3. Document everything: Record decisions and rationale
4. Version control: Track all changes

Evaluation Phase

1. Multi-dimensional: Evaluate on multiple criteria
2. Automated + human: Combine automatic and manual evaluation
3. Edge cases: Test with challenging inputs
4. Regression testing: Ensure changes don't break existing functionality

Deployment Phase

1. Gradual rollout: Deploy to small audiences first
2. Monitoring: Track all key metrics
3. Fallbacks: Have backup plans for failures
4. Cost tracking: Monitor and optimize expenses

Operations Phase

1. Regular audits: Review quality and safety
2. Continuous improvement: Iterate based on feedback
3. Knowledge sharing: Document learnings
4. Stay current: Keep up with field advances

Practice Exercises

1. Prompt Optimization: Take a poorly performing prompt and improve it using the best practices outlined here. Measure the improvement.

2. Evaluation Design: Design an evaluation framework for an LLM application. What metrics and methods would you use?

3. Cost Analysis: Analyze the cost structure of an LLM application. What optimization strategies would you implement?

4. Safety Audit: Conduct a safety audit of an LLM system. What vulnerabilities did you find?

What to Learn Next