Sample Expert Audit Report
Example of what you receive with the £299 audit
Professional Analysis•Custom Code Fixes•Implementation Guidance
Executive Summary
Current Spend
£4,250/mo
Based on 150K tokens/month
Potential Savings
£1,820/mo
43% reduction possible
Implementation Time
2-3 days
Estimated rollout time
Key Findings
- 40% of requests use GPT-4 when GPT-3.5 would suffice
- Missing response caching for repeated queries
- Suboptimal prompt engineering increasing token usage
Cost Breakdown
By Model
GPT-4
£2850
67%
GPT-3.5 Turbo
£950
22%
Embeddings
£320
8%
Fine-tuning
£130
3%
By Usage Pattern
Real-time queries
Optimization potential
£2100
49%
Batch processing
Optimization potential
£1200
28%
Development/testing
Optimization potential
£650
15%
Caching opportunities
Optimization potential
£300
8%
Recommended Fixes
1. Model Selection Optimization
// BEFORE: Always using GPT-4
const response = await openai.chat.completions.create({
model: "gpt-4",
messages: [{ role: "user", content: userInput }],
max_tokens: 1000
});
// AFTER: Smart model selection based on complexity
const model = shouldUseGPT4(userInput) ? "gpt-4" : "gpt-3.5-turbo";
const response = await openai.chat.completions.create({
model,
messages: [{ role: "user", content: userInput }],
max_tokens: 1000
});
function shouldUseGPT4(input) {
// Logic to determine if GPT-4 is actually needed
return input.length > 5000 ||
containsComplexTask(input) ||
requiresLatestKnowledge(input);
}Expected Savings: £1,200/month (42% reduction in model costs)
2. Response Caching Implementation
// BEFORE: No caching
async function getAnswer(question) {
const response = await openai.chat.completions.create({
model: "gpt-3.5-turbo",
messages: [{ role: "user", content: question }]
});
return response.choices[0].message.content;
}
// AFTER: Redis-based caching
const redis = new Redis(process.env.REDIS_URL);
async function getAnswer(question) {
const cacheKey = `answer:${hash(question)}`;
const cached = await redis.get(cacheKey);
if (cached) {
return JSON.parse(cached);
}
const response = await openai.chat.completions.create({
model: "gpt-3.5-turbo",
messages: [{ role: "user", content: question }]
});
const answer = response.choices[0].message.content;
await redis.setex(cacheKey, 3600, JSON.stringify(answer)); // 1 hour TTL
return answer;
}Expected Savings: £450/month (60% reduction in repeated queries)
3. Prompt Engineering
// BEFORE: Inefficient prompt
const prompt = `Answer this question: ${question}. Be very detailed and thorough.`;
// AFTER: Optimized prompt with clear constraints
const prompt = `Answer concisely in 2-3 sentences:
Question: ${question}
Constraints: Max 150 tokens, focus on key points only.`;Expected Savings: £170/month (30% reduction in token usage)
Implementation Timeline
Week 1
- Implement model selection logic
- Set up Redis caching infrastructure
Week 2
- Deploy caching layer
- Optimize prompt templates
Week 3
- Monitor savings and adjust
- Document new patterns