AI Performance Optimization Guide¶

This guide covers techniques for optimizing the performance of AI models in the AI Assistant System.

Overview¶

Optimizing AI performance involves balancing response quality, speed, and cost. This guide provides strategies for improving efficiency while maintaining or enhancing output quality.

Response Time Optimization¶

1. Model Selection¶

Choose the right model for your use case:

# For simple tasks
model = "gpt-3.5-turbo"  # Faster, cheaper

# For complex reasoning
model = "gpt-4"  # Higher quality, slower

# For local deployment
model = "llama2-7b"  # Variable speed based on hardware

2. Request Batching¶

Process multiple requests together:

from app.core.optimization import BatchProcessor

batch_processor = BatchProcessor(
    batch_size=10,
    wait_time=0.5  # seconds
)

# Add requests to batch
batch_processor.add_request(prompt1, callback1)
batch_processor.add_request(prompt2, callback2)

# Process batch when full or timeout
await batch_processor.process_batch()

3. Streaming Responses¶

Stream responses for better user experience:

from app.core.optimization import StreamProcessor

stream_processor = StreamProcessor()

async def stream_response(prompt):
    async for chunk in stream_processor.generate_stream(prompt):
        yield chunk

4. Parallel Processing¶

Process independent requests in parallel:

import asyncio

async def process_multiple_prompts(prompts):
    tasks = [generate_response(prompt) for prompt in prompts]
    responses = await asyncio.gather(*tasks)
    return responses

Cost Optimization¶

1. Token Management¶

Minimize token usage:

from app.core.optimization import TokenOptimizer

optimizer = TokenOptimizer()

# Compress prompts
compressed_prompt = optimizer.compress_prompt(original_prompt)

# Optimize output format
optimized_format = optimizer.optimize_format(response_format)

2. Smart Caching¶

Implement intelligent caching:

from app.core.optimization import SmartCache

cache = SmartCache(
    ttl=3600,  # 1 hour
    max_size=1000,
    similarity_threshold=0.9  # Cache similar responses
)

@cache.cached_response
async def generate_response(prompt):
    # Check cache first
    cached = cache.get_similar(prompt)
    if cached:
        return cached

    # Generate new response
    response = await model.generate(prompt)
    cache.store(prompt, response)
    return response

3. Model Routing¶

Route requests to cost-effective models:

from app.core.optimization import CostRouter

router = CostRouter()

# Define cost tiers
router.add_tier("cheap", ["gpt-3.5-turbo"], cost_per_token=0.000002)
router.add_tier("balanced", ["gpt-4"], cost_per_token=0.00003)
router.add_tier("premium", ["claude-3-opus"], cost_per_token=0.000075)

# Route based on budget
model = router.select_model(budget=0.01, complexity=0.7)

4. Usage Monitoring¶

Track and control usage:

from app.core.optimization import UsageMonitor

monitor = UsageMonitor(daily_limit=100.0)  # $100 daily limit

@monitor.track_usage
async def generate_response(prompt):
    current_cost = monitor.get_daily_cost()
    if current_cost > monitor.daily_limit * 0.9:
        # Switch to cheaper model
        model = "gpt-3.5-turbo"

    return await model.generate(prompt)

Quality Optimization¶

1. Prompt Engineering¶

Optimize prompts for better results:

from app.core.optimization import PromptOptimizer

prompt_optimizer = PromptOptimizer()

# A/B test prompts
results = await prompt_optimizer.test_prompts(
    original_prompt,
    optimized_prompt,
    test_cases=test_data
)

# Use the better performing prompt
best_prompt = results.get_best_prompt()

2. Response Filtering¶

Filter and improve responses:

from app.core.optimization import ResponseFilter

filter = ResponseFilter()

async def generate_filtered_response(prompt):
    response = await model.generate(prompt)

    # Check quality metrics
    if not filter.meets_quality_standards(response):
        # Retry with different parameters
        response = await model.generate(
            prompt,
            temperature=0.3,  # Lower temperature for more consistent output
            max_tokens=1500   # Adjust token limit
        )

    return filter.enhance_response(response)

3. Ensemble Methods¶

Combine multiple model outputs:

from app.core.optimization import EnsembleModel

ensemble = EnsembleModel(models=["gpt-4", "claude-3-opus"])

async def generate_ensemble_response(prompt):
    responses = await ensemble.generate_all(prompt)

    # Select best response
    best_response = ensemble.select_best(responses, criteria="quality")

    # Or combine responses
    combined_response = ensemble.combine(responses)

    return best_response

Resource Optimization¶

1. Connection Pooling¶

Reuse connections for efficiency:

from app.core.optimization import ConnectionPool

pool = ConnectionPool(
    min_connections=5,
    max_connections=20,
    connection_timeout=30
)

async def generate_with_pool(prompt):
    async with pool.get_connection() as conn:
        return await conn.generate(prompt)

2. Memory Management¶

Optimize memory usage:

from app.core.optimization import MemoryManager

memory_manager = MemoryManager(max_memory_gb=4)

@memory_manager.optimize_memory
async def process_large_text(text):
    # Process in chunks
    chunks = memory_manager.chunk_text(text, chunk_size=1000)
    results = []

    for chunk in chunks:
        result = await model.generate(chunk)
        results.append(result)

        # Clear memory
        memory_manager.clear_cache()

    return memory_manager.combine_results(results)

3. GPU Optimization¶

For local models:

from app.core.optimization import GPUOptimizer

gpu_optimizer = GPUOptimizer()

# Enable mixed precision
gpu_optimizer.enable_mixed_precision()

# Optimize batch sizes
optimal_batch_size = gpu_optimizer.find_optimal_batch_size()

Monitoring and Analytics¶

1. Performance Metrics¶

Track key performance indicators:

from app.core.optimization import PerformanceTracker

tracker = PerformanceTracker()

@tracker.track_performance
async def generate_response(prompt):
    start_time = time.time()
    response = await model.generate(prompt)
    end_time = time.time()

    tracker.record_metrics({
        "response_time": end_time - start_time,
        "token_usage": response.usage.total_tokens,
        "model": model.name,
        "prompt_length": len(prompt)
    })

    return response

2. Quality Metrics¶

Measure response quality:

from app.core.optimization import QualityMetrics

quality_metrics = QualityMetrics()

def evaluate_response(prompt, response, expected):
    return {
        "accuracy": quality_metrics.check_accuracy(response, expected),
        "relevance": quality_metrics.check_relevance(prompt, response),
        "completeness": quality_metrics.check_completeness(prompt, response),
        "coherence": quality_metrics.check_coherence(response)
    }

3. Cost Analysis¶

Analyze cost efficiency:

from app.core.optimization import CostAnalyzer

analyzer = CostAnalyzer()

def analyze_cost_efficiency(responses):
    return {
        "cost_per_response": analyzer.calculate_cost_per_response(responses),
        "cost_per_quality_point": analyzer.calculate_cost_per_quality(responses),
        "most_cost_effective_model": analyzer.find_most_efficient(responses)
    }

Advanced Optimization Techniques¶

1. Predictive Caching¶

Pre-cache likely requests:

from app.core.optimization import PredictiveCache

predictive_cache = PredictiveCache()

# Learn from usage patterns
predictive_cache.learn_from_history(usage_history)

# Pre-cache likely requests
predicted_requests = predictive_cache.predict_next_requests()
for request in predicted_requests:
    await predictive_cache.pre_cache(request)

2. Adaptive Model Selection¶

Choose models based on request complexity:

from app.core.optimization import AdaptiveSelector

selector = AdaptiveSelector()

async def generate_adaptive_response(prompt):
    # Analyze prompt complexity
    complexity = selector.analyze_complexity(prompt)

    # Select appropriate model
    if complexity < 0.3:
        model = "gpt-3.5-turbo"
    elif complexity < 0.7:
        model = "gpt-4"
    else:
        model = "claude-3-opus"

    return await model.generate(prompt)

3. Dynamic Parameter Tuning¶

Adjust parameters based on performance:

from app.core.optimization import DynamicTuner

tuner = DynamicTuner()

async def generate_with_tuning(prompt):
    # Get current performance metrics
    metrics = tuner.get_current_metrics()

    # Adjust parameters
    if metrics["error_rate"] > 0.1:
        temperature = 0.2  # Lower temperature for consistency
    elif metrics["response_time"] > 5.0:
        max_tokens = 1000  # Reduce tokens for speed

    return await model.generate(
        prompt,
        temperature=temperature,
        max_tokens=max_tokens
    )

Best Practices¶

1. Start Simple¶

Begin with basic optimizations and measure impact before implementing complex solutions.

2. Measure Everything¶

You can't optimize what you don't measure. Implement comprehensive monitoring.

3. Balance Trade-offs¶

Understand the trade-offs between speed, cost, and quality for your use case.

4. Test Continuously¶

Regularly test optimizations to ensure they're having the desired effect.

5. Document Changes¶

Keep track of optimizations and their impacts for future reference.

Troubleshooting¶

Common Issues¶

Slow Responses: Check model selection, batching, and caching
High Costs: Review token usage, model selection, and caching strategies
Poor Quality: Examine prompts, model parameters, and consider ensemble methods
Resource Exhaustion: Implement proper resource management and monitoring

Debug Mode¶

Enable detailed logging for optimization:

import logging
logging.getLogger("app.core.optimization").setLevel(logging.DEBUG)

Conclusion¶

Optimizing AI performance is an ongoing process that requires continuous monitoring and adjustment. Start with the techniques that address your biggest pain points, and gradually implement more sophisticated optimizations as needed.

Remember that the best optimization strategy depends on your specific use case, requirements, and constraints. Regularly review and adjust your approach based on performance data and changing needs.