How we built user behavior analysis with multi-modal LLMs (in 5 not-so-easy steps)

PostHog has introduced Session Summaries, a tool designed to efficiently analyze user behavior data by summarizing user sessions and highlighting issues without manual review. This tool leverages large language models (LLMs) to process billions of events and terabytes of data, focusing on essential session elements to avoid overwhelming the LLM with unnecessary metadata. The approach involves processing entire sessions in one call to preserve context, filtering out non-critical events like spurious exceptions, and using video clips to confirm issues flagged by summaries. Although the tool is in beta, it aims to identify recurring user issues and provide visual validation through session recordings. The development process faced challenges such as managing vast amounts of data, ensuring session context, and dealing with potential errors in LLM responses. These challenges are mitigated through techniques like caching, temporal workflows, and a phased analysis pipeline. Future enhancements include full video understanding, proactive alerts, and integration with additional data sources like error tracking and support tickets.

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