Transforming AI Efficiency: Our Journey to Semantic Tool Selection
When I began building our AI assistant, it featured just five tools. Today, we’ve expanded to nearly 40, but with this growth came unexpected challenges.
Key Takeaways:
- Cost Reduction: We slashed our token usage by 60-80% by embedding tool descriptions and minimizing irrelevant data.
- Increased Speed: Latency improved significantly by approximately 200ms, allowing for quicker responses.
- Enhanced Accuracy: With fewer tools to consider, the model selects more relevant options, enhancing user experience.
The Solution:
- Semantic Selection: We used embeddings to find the most relevant tools, making queries efficient.
- Flexibility: We opted for OpenAI’s embedding service for reliability while maintaining options for a local model in development.
The Impact:
Our costs are minimal—around $0.01 per day, with the implementation adding only 10ms to the response time!
For anyone facing tool explosion in AI agents, consider this approach!
👉 Let’s share insights on optimizing AI! Comment below with your experiences!
