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How to reduce AI costs while maintaining quality

💡 Best Practices • Started by Maria Rodriguez ✓ • 2 weeks ago

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💬 156 Replies

⭐ 89 Solutions

2 weeks ago

I've been using Chat247 for 6 months and my OpenAI bills were getting out of control. After lots of testing, I found strategies that cut costs by 60% without sacrificing quality.

**Key strategies:**
1. Prompt optimization - shorter, more specific prompts
2. Smart caching - reuse common responses
3. Model selection - use GPT-3.5 for simple queries
4. Response streaming - better UX and early termination

Happy to share detailed examples if there's interest!

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3 Replies

2 weeks ago

This is gold! I've been struggling with the same issue. Can you share more about your caching strategy? Are you using Redis or something built into Chat247?

2 weeks ago ✅ Solution

We use a simple in-memory cache for the most common 50 queries. Here's the pseudo-code:

```python
if query in common_queries:
return cached_response
else:
response = call_openai_api(query)
cache_if_useful(query, response)
```

The key is identifying which queries to cache. We analyze our logs weekly.

2 weeks ago

What's your criteria for "useful" when deciding to cache? Message frequency? User satisfaction scores?

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