EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Jul 23·5 min read
Prompt Caching In Agents
TL;DR
Prompt caching in coding agents optimizes the reuse of input data, reducing computational costs and latency. However, changes in tool definitions or model configurations can disrupt this efficiency.
✦ Why It Matters
Evaluate your tool definitions and model settings to maintain efficient prompt caching.
Key Takeaways
- Implement session affinity for faster cache access.
- Avoid frequent changes to tool definitions to maintain cache efficiency.
- Consider distributed caching for improved flexibility.
- Monitor cache performance to reduce unexpected costs.
How It Works
Prompt caching allows coding agents to reuse previously computed key-value (KV) states, reducing the need to reprocess large prompts. When a new request starts with the same tokens as a previous one, the system can leverage the stored KV cache to quickly generate responses, only processing new input.