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Developer Guide

Developer Strategies for AI Efficiency

Developers can build responsible AI applications by optimizing prompts to reduce token usage, implementing semantic caching to minimize redundant API calls, and selecting smaller, task-specific models. These strategies reduce compute costs, lower energy consumption, and improve application latency for sustainable AI development.

Quick Wins

40-60%

Cost reduction with caching

2-4x

Faster with smaller models

30%

Token savings with prompt optimization

90%

Tasks work with smaller models

Optimize Prompts

Reduce token count without sacrificing quality.

  • • Use concise system prompts—every token costs money
  • • Avoid redundant context and preambles
  • • Use few-shot examples only when necessary
  • • Test shorter prompts for equivalent results 
// Before: 156 tokens
"You are a helpful assistant. Please help the user with their question. Be friendly and thorough in your response."

// After: 23 tokens  
"You are a concise technical assistant."

Implement Caching

Avoid redundant API calls for similar queries.

  • • Cache common responses with Redis/Memcached
  • • Use semantic caching for similar queries
  • • Set appropriate TTLs based on content freshness
  • • Monitor cache hit rates and optimize 
// Semantic cache example
const cache = new SemanticCache({ 
  similarity: 0.95 
});

async function query(prompt) {
  const cached = await cache.get(prompt);
  if (cached) return cached;
  
  const response = await llm.complete(prompt);
  await cache.set(prompt, response);
  return response;
}

Rate Limiting

Control costs and prevent abuse.

  • • Set per-user rate limits
  • • Implement tiered access based on plans
  • • Use token buckets for smooth limiting
  • • Alert on unusual usage patterns 
// Token bucket rate limiter
const limiter = new RateLimiter({
  tokensPerInterval: 100,
  interval: 'minute',
});

async function handler(req, res) {
  const remaining = await limiter.removeTokens(1);
  if (remaining < 0) {
    return res.status(429).json({ 
      error: 'Rate limit exceeded' 
    });
  }
  // Process request...
}

Choose the Right Model

Match model capabilities to task requirements.

  • • Use smaller models for simple tasks (classification, extraction)
  • • Reserve large models for complex reasoning
  • • Test model performance vs. cost tradeoffs
  • • Consider fine-tuned smaller models 
// Model selection by task
const modelMap = {
  classification: 'gpt-3.5-turbo',
  summarization: 'gpt-3.5-turbo',
  complex_reasoning: 'gpt-4',
  code_generation: 'gpt-4',
};

function getModel(task) {
  return modelMap[task] || 'gpt-3.5-turbo';
}

Explore the Knowledge Base

Technical guides on prompt optimization, caching strategies, and model selection.

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