From: aidotengineer
Evaluating AI models, especially large language models (LLMs), is inherently challenging. LLMs are prone to hallucination and are difficult to evaluate, particularly in conversational settings where objective metrics are scarce [00:00:21]. When dealing with audio and voice agents, these challenges intensify, making development even more arduous [00:00:53].
Challenges in AI Agent Evaluation
Building robust AI applications, especially voice agents, goes beyond simple API calls and prompt engineering [00:17:31]. Several issues highlight the need for comprehensive evaluation:
- Difficulty in Measuring Performance It’s challenging to ascertain how well an AI system is performing, especially in conversational AI where there’s no perfect “ground truth” or objective historical data to measure against [00:14:18], [00:15:26].
- Agent Behavior Challenges AI agents often struggle to maintain a balance, either following up too little or too much [00:08:22]. They might rephrase questions in unhelpful ways or get stuck in “rabbit holes” of chitchat [00:07:01], [00:08:31].
- Transcription Inaccuracies Voice models, even with advanced tools like OpenAI’s Whisper, can produce surprising or nonsensical transcripts from silence or background noise, impacting user experience [00:11:19], [00:11:51].
- Complexity from Band-Aid Solutions Incrementally adding multiple agents to fix issues (e.g., drift detection, next question agents, transcript hiding agents) leads to a complex system with many prompts, making debugging difficult and increasing the risk of introducing regressions [00:13:28], [00:13:57].
Solutions and Best Practices for AI Evaluation
To address these challenges, a systematic approach to evaluation is crucial:
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Define Metrics and Automated Test Suites
- Develop a set of specific metrics to measure desired attributes of the conversation (e.g., clarity, completeness, professionalism) [00:14:31].
- Implement an automated test suite that uses an LLM as a “judge” to score conversations against these metrics [00:14:37], [00:14:42]. This provides a more objective, metrics-driven iteration process, moving away from subjective “vibes-driven” development [00:15:01], [00:15:15].
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Utilize Synthetic Conversations for Testing
- Even without perfect ground truth data, create synthetic conversations by using LLMs to simulate various user personas (e.g., “snarky teenager,” different job functions) [00:16:15], [00:16:41].
- Run the AI agent through many synthetic interviews to measure performance against a broad population of expected users and automate testing [00:16:23], [00:17:14].
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Implement Out-of-Band Checks and Tool Use
- Introduce separate “side agents” (e.g., a “drift detector” or “next question agent”) operating in the text domain to listen to the conversation transcript and make decisions about the conversation’s direction, relevance, or progression [00:07:33], [00:07:49], [00:09:25], [00:17:55].
- Use “tool use” to constrain and instrument LLM behavior, requiring the LLM to call specific tools to advance the conversation, thereby providing insights into its state and intentions [00:06:00], [00:18:10].
- When adding goals and priorities as first-class concepts to interview plans, inform the LLM not just what to ask but why, helping it guide follow-up questions and rephrasing more effectively [00:09:04], [00:10:07].
Evaluations are critical for measuring success and guiding development in all LLM-based projects [00:18:28]. Even in domains without objective truth, harnessing evaluations can lead to a robust development process [00:18:37].