Generative AI for software engineers is more than code completion

What this post added

This post introduces and elaborates on the 'two-layer model' for generative AI in software engineering, emphasizing the critical role of a 'context layer' beneath coding surfaces. It details how this context layer, powered by Glean's knowledge graph and enterprise graph capabilities, connects disparate engineering systems (code repositories, Jira, Slack, observability tools, design docs, wikis) to provide AI tools with comprehensive, permission-aware context. This enables AI to move beyond code completion to assist with finding context, navigating incidents, and shipping safely. The post explains how this architecture addresses the 'AI productivity paradox' by solving the bottleneck of context assembly, which slows down developers and impacts onboarding, daily coding, and incident response. It highlights Glean's role in indexing code and documents with hybrid search, building an enterprise graph linking services, APIs, incidents, tickets, and owners, and respecting security and governance boundaries.