Build Hour: Prompt Caching

By OpenAI

Categories: AI, Product

Summary

Prompt caching can reduce latency by up to 67% and costs by up to 90% with no impact on model performance, making it a 'no-brainer' for sophisticated AI applications.

Key Takeaways

1. Prompt caching automatically caches 1024+ token inputs in 128 token blocks, requiring contiguous prefixes to qualify for a cache hit.
2. Cash hit rates can drive up to 75% cost savings on model calls and 67% faster latency, with the largest gains on prompts over 1024 tokens.
3. Maximize cache hit rates by optimizing prompt caching keys, engineering context, selecting the right endpoints, and using the extended prompt caching feature.
4. Warp, a customer case study, achieved a 93% cache hit rate and 85% cost savings by implementing prompt caching in their AI workflows.
5. Prompt caching is a 'no-brainer' tactic for sophisticated AI applications, enabling huge latency and cost savings with no impact on model performance.
6. Prompt caching is available automatically across OpenAI's text, image, and audio models, with no code changes required by developers.

Topics

• Prompt Caching
• Cost Optimization
• Latency Reduction
• AI Workflow Optimization
• OpenAI API Best Practices

Transcript Excerpt

Hey everyone, welcome back to OpenAI Build Hours. I'm Christine. I'm on the startup marketing team and today I'm here with Erica. >> Hi, I'm a solutions engineer at OpenAI. >> So today's session is about prompt caching. Um, and this is one of the fastest ways to cut latency and reduce costs. So I'm really excited we're diving deep into this topic today. So quick context on what build hours are all about. Um, it's really to empower you with the best practices, tools, and AI expertise to scale you...