The original title is about optimizing language models with cost vs performance trade-offs. Let me rewrite this for a mobile feed.

Short summary

To optimize LLM costs in production, tier your models by task complexity—GPT-4 for complex reasoning, GPT-3.5 for moderate tasks, local models for classification. Pair this with prompt optimization (5-10x token reduction), aggressive caching (90%+ savings), fine-tuning, streaming, and batch processing. These strategies reduce costs 60-80% while maintaining quality, but require cost tracking across requests and features.

• •Use model tiering by task complexity to reduce costs 60-80%
• •Optimize prompts, cache aggressively, and fine-tune for domain-specific work
• •Implement cost monitoring dashboards to track per-request, per-feature, per-user metrics

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