IT decision makers should evaluate both prompt caching and Retrieval-Augmented Generation (RAG) as complementary tools in their LLM strategy. Prompt caching brings speed and savings, while RAG delivers context-rich accuracy. Plan for both, instead of choosing between them.
Why You Should Care
- Prompt caching reduces costs and speeds up inference. It trims token usage, which directly lowers inference fees and boosts response times. It is ideal for stable datasets and high-traffic, repetitive queries.
- RAG still reigns for data scale and accuracy. It shines when you’re working with large, dynamic, or constantly evolving datasets. RAG can feed LLMs more current and relevant data, reducing hallucinations and increasing trust in outputs.
- Complexity is the trade-off. Prompt caching is easy to deploy, while RAG requires more heavy lifting.
What You Should Do Next
- Use RAG for applications requiring up-to-date and comprehensive context.
- Use prompt caching where latency and cost-efficiency matter most.
- Explore hybrid approaches by using prompt caching for quick retrievals with RAG as the fallback for complex queries.
Get Started
- Review your AI workloads to identify which use cases require speed and which demand precision.
- Pilot prompt caching with your most repetitive LLM queries to discover cost and latency benefits.
- Begin designing a RAG workflow for complex or frequently updated domains.
- Reach out to your AI provider for timelines and integration support on prompt caching features, if this feature is not available yet.