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Hamming
Overview :
Hamming is an end-to-end AI voice agent testing platform that supports the entire workflow from development to production. It automates the creation of voice roles to generate thousands of concurrent phone calls, identifying bugs in voice agents and significantly enhancing testing efficiency. In addition, Hamming offers prompt management, optimization, and a playground, enabling users to automatically generate optimized prompts and test LLM outputs on datasets. Hamming also features active monitoring capabilities that track and score AI application usage in production, flagging cases that need attention.
Target Users :
Hamming targets enterprise teams in high-risk sectors that require rapid and reliable development and testing of AI voice agents. These teams may include AI engineers, data scientists, product engineers, and operations engineers, all needing to ensure the accuracy and robustness of AI agents to avoid potential high customer attrition or regulatory consequences.
Features
Automated voice testing: Create concurrent phone calls with voice roles to discover bugs in voice agents.
Prompt management: Store, manage, version control, and synchronize prompts for different clients.
Prompt optimizer and playground: Automatically generate optimized prompts and test LLM outputs, saving manual engineering effort.
Active monitoring: Track and score the usage of AI applications, marking cases that require attention.
Cross-team collaboration: Facilitate collaboration among different team members.
Scenario creation: Develop scenarios and roles to test entire conversation spaces.
Experiment tracking: Monitor experimental hypotheses, proposed changes, and learning outcomes.
How to Use
1. Register and log in to the Hamming platform.
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2. Create or import an AI voice agent for testing.
3. Use Hamming's voice roles to generate concurrent phone calls and test the AI agent.
4. Utilize the prompt management feature to store and manage prompts for different clients.
5. Employ the prompt optimizer to automatically generate optimized prompts.
6. Test LLM outputs in the playground and adjust prompts as necessary.
7. Use the active monitoring feature to track the AI application's usage in production.
8. Analyze test results and iterate optimizations based on feedback.
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