"Datadog's innovative use of LLMs in incident postmortem creation combines structured data and Slack messages to enhance report quality and efficiency."
"The team invested over 100 hours fine-tuning their model instructions and structure to ensure accurate and high-quality outputs for incident postmortems."
"Exploring model variants like GPT-3.5 and GPT-4 revealed significant trade-offs; GPT-4 was more accurate but also slower and costlier than GPT-3.5."
"By running LLM tasks in parallel and selecting model versions based on content complexity, Datadog reduced postmortem report generation from 12 minutes to under a minute."
Datadog has innovatively integrated large language models (LLMs) with structured metadata and Slack messages to streamline the incident postmortem creation process. The team faced challenges in ensuring high-quality content while adapting LLMs beyond typical dialog systems. After extensive fine-tuning over 100 hours, they evaluated multiple models, including GPT-3.5 and GPT-4, to balance cost, speed, and accuracy. This allowed for a significant reduction in report generation time from 12 minutes to under 1 minute by executing tasks in parallel and addressing trust concerns by marking AI-generated content clearly. Moreover, sensitive data was omitted from the inputs to protect privacy.
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