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Meta Uses LLMs to Improve Incident Response
How Meta Uses LLMs to Improve Incident Response (and how you can too) - Meta used LLMs to root cause incidents with 42% accuracy. Here's how they did it and how you can do it too.
In the SFT phase, Meta mixed the original training data of Llama 2 with its own root cause analysis dataset, which focused on instruction-tuning examples, enabling the model to follow RCA-related prompts effectively. This fine-tuning process, combined with the aggregation of new datasets, allows Meta's LLM to significantly improve the accuracy of its root cause predictions, achieving a 42% success rate in identifying the culprit code changes during investigations. They can also begin to handle more of the incident response process and workflow (find and follow runbooks, measure impact, take mitigation steps, create code changes, write initial post-mortems).
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