Optimization
Optimize model performance and costs
Model Optimization
Optimize your AI model usage for better performance and lower costs.
Cost Optimization
Smart Model Routing
Route requests to appropriate models:
{
"model": {
"routing": {
"simple": "llama-3",
"medium": "gemini-pro",
"complex": "gpt-4"
}
}
}Implement Caching
Cache responses to reduce API calls:
@agent.cache(ttl=3600)
async def get_response(query):
return await agent.generate(query)Optimize Token Usage
Reduce input and output tokens:
# Bad: Verbose prompt (1000 tokens)
prompt = f"""
You are a helpful AI assistant...
[Long instructions]
Question: {question}
"""
# Good: Concise prompt (100 tokens)
prompt = f"Q: {question}\nA:"Performance Optimization
Use Streaming
Stream responses for better UX:
async for chunk in agent.generate_stream(prompt):
yield chunkParallel Processing
Process multiple requests in parallel:
responses = await asyncio.gather(
agent.generate(prompt1),
agent.generate(prompt2),
agent.generate(prompt3)
)Batch Requests
Batch similar requests:
responses = await agent.generate_batch([
prompt1, prompt2, prompt3
])Quality Optimization
Temperature Tuning
Adjust temperature for different use cases:
{
"model": {
"temperature": 0.7, // Creative tasks
"temperature": 0.2 // Factual tasks
}
}Context Window Management
Optimize context usage:
# Summarize old context
if len(context) > 4000:
context = await agent.summarize(context)Prompt Engineering
Use effective prompts:
# Good: Clear, specific prompt
prompt = """
Task: Summarize the following text in 3 bullet points.
Text: {text}
Summary:
"""Monitoring
Track Model Performance
Monitor key metrics:
swiftclaw metrics my-agent \
--metric response-time \
--metric cost-per-request \
--metric quality-scoreA/B Testing
Compare model performance:
# Test different models
results = await agent.ab_test(
models=["gpt-4", "claude-3-sonnet"],
prompt=prompt,
sample_size=100
)Continuous optimization can reduce costs by 60-80% while maintaining quality.
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