AI-curated insights from 1000+ daily updates, delivered as an audio briefing of new capabilities, real-world cases, and product tools that matter.
Stay ahead with AI-curated insights from 1000+ daily and weekly updates, delivered as a 7-minute briefing of new capabilities, real-world cases, and product tools that matter.
Join The GenAI PMDive deeper into the topics covered in today's brief with these AI PM insights.
To assess the Perplexity Search API for enhancing your LLM-powered products, start by focusing on its core promise: delivering millisecond-latency search results that ground large language models (LLMs) and agents with real-time web data. Begin by identifying the key performance indicators (KPIs) that are critical to your product’s success, such as response time, accuracy, and relevance of the search results. Once these metrics are defined, run a series of controlled experiments using real-world scenarios that mirror your target use cases. For example, test how the API supports dynamic query generations during live chats or how it improves the performance of your bot in responding to emerging events. Integrate the API with your development environment and closely monitor API calls, latency, and error rates to ensure that any increase in performance does not compromise the reliability of your overall system. Further, the API’s real-time capability provides an edge in competitive markets by offering up-to-the-minute information, so benchmarking against existing search solutions in your product can uncover technical advantages and gaps. Collaborate with data engineers and system architects to simulate high-traffic conditions and evaluate scalability. Finally, consider user feedback loops and implement logging and analytics systems to track user experiences post-integration. Such actionable insights will complement the quantitative test results and help fine-tune the integration strategy over time. This comprehensive assessment ensures that the real-time data delivered by the Perplexity Search API not only meets your technical standards but also enriches your user experience by enabling timely, context-aware decision-making.
Building effective AI evaluations has become a crucial skill for AI product managers, as evidenced by the recent deep dive into constructing AI evals frameworks. Start by conducting a manual error analysis on a significant sample of your application traces, which provides the foundational data needed for the process. For instance, as suggested by experts in the evals course, begin with open coding—review around 100 independent traces to identify visible issues, such as hallucinations or formatting errors. When you reach a point of saturation where no new failure modes are emerging, organize these initial observations into meaningful clusters, known as axial codes. This classification (e.g., 'tour scheduling errors' or 'data formatting issues') helps in prioritizing which issues should be tackled first. Moving forward, integrate automation into your evaluation by developing core code-based evaluators that can automatically flag simple error scenarios. Additionally, meeting the challenge of nuanced failure modes might require leveraging LLM-as-judge evals, where the language model itself compares outputs against predefined human-labeled benchmarks. Such an automated pipeline can be integrated into your continuous integration (CI) system, ensuring that any new product iterations maintain the quality standards set by your evaluation metrics. The key takeaway for PMs here is to use these data-driven insights not only for immediate troubleshooting but also as a strategic feedback loop for future development cycles. By systematically tracking how each iteration of your product responds to these evaluations, you can continually optimize reliability and performance, thereby reinforcing your product’s competitive edge in a rapidly evolving market.