Optimizing a Drive-Thru Voice Agent with Synthetic Data and Simulation
The article describes the author's process of building and testing a drive-thru voice agent, including using synthetic data, baseline prompts, and simulation to identify and fix issues before deploying to real users.
Why it matters
Optimizing voice agents before deployment is critical to ensure a seamless user experience and avoid production blockers.
Key Points
- 1Used synthetic data generator to create 500 diverse drive-thru interactions for testing
- 2Ran baseline prompts on multiple language models to assess initial accuracy and response quality
- 3Identified issues like latency, logic breaks, and low success rate (66%) through simulation testing
- 4Leveraged automated optimization techniques to improve the agent's performance
Details
The author built a drive-thru voice agent called 'Future Burger' that focused on the intelligence layer rather than just the speech-to-text and text-to-speech components. To test the agent before deploying to real users, the author used a synthetic data generator to create 500 diverse drive-thru interactions with labeled inputs and expected outputs. This quickly exposed gaps in the agent's logic, such as handling mid-sentence order changes and multilingual switches. The author then ran baseline prompts on multiple language models, which showed 80% accuracy but overly verbose responses. Simulation testing revealed further issues with latency and logic breaks, resulting in a 66% success rate. The author then leveraged automated optimization techniques to improve the agent's performance, ultimately achieving a 96% success rate.
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