基本信息
- 来源: blogs_podcasts
- 原始来源: https://engineering.fb.com/2026/04/06/developer-tools/how-meta-used-ai-to-map-tribal-knowledge-in-large-scale-data-pipelines
来源摘要/节选
公开展示已截断至最多 800 个字符；请访问原始来源查看完整上下文。
AI coding assistants are powerful but only as good as their understanding of your codebase. When we pointed AI agents at one of Meta’s large-scale data processing pipelines – spanning four repositories, three languages, and over 4,100 files – we quickly found that they weren’t making useful edits quickly enough.
We fixed this by building a pre-compute engine: a swarm of 50+ specialized AI agents that systematically read every file and produced 59 concise context files encoding tribal knowledge that previously lived only in engineers’ heads. The result: AI agents now have structured navigation guides for 100% of our code modules (up from 5%, covering all 4,100+ files across three repositories).…
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