Large language models can generate long-form technical incident reports with significant inaccuracies, unusual conclusions, and inconsistent writing styles. The underlying cause is their autocomplete-like behavior that makes educated guesses. Errors can be reduced by using granular, single-task instructions focused on small portions of a report, specifying which sources to use, and setting rules for style and format. Cisco reports that these techniques cut drafting time for incident reports from tabletop exercises by 50% while maintaining writing quality in blind testing. Peer reviewers and editors provided positive feedback and noted fewer typos and grammatical errors. Editing multiple sample reports in a single session can cause cross-contamination of content between reports.
""The peer reviewer commented that the incidence of typos and grammatical errors was far lower than in the average report." But the Talos team also found "editing multiple sample reports within a single session resulted in cross-contamination of content from one repo"
#ai-generated-reporting #incident-response #llm-prompting #security-documentation #quality-assurance
Read at theregister
Unable to calculate read time
Collection
[
|
...
]