From transcript to actionable notes: Building effective LLM pipelines for meeting intelligence

Ani Ghazaryan's guide on building effective LLM pipelines for meeting intelligence emphasizes the importance of a modular approach to transcription and extraction. It outlines how many AI note-taker pipelines fail because they combine transcription and extraction into a single LLM process, leading to errors like hallucinated action items and misattributions. The recommended approach involves using asynchronous transcription with speaker diarization and word-level timestamps as a foundation, followed by separate LLM stages for summarization, action item extraction, and decision logging, all enforced through JSON schemas. The guide details the architectural design needed to transform raw audio into structured, verifiable JSON outputs and discusses managing pipeline states, ensuring timestamp integrity, and dealing with code-switching. It also highlights the necessity of managing pipeline latency, costs, and troubleshooting extraction failures, while emphasizing the reliability of Gladia’s async API for accurate diarized transcripts and error reduction.

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