Building an AI Context Layer for Engineering Teams
In a previous post, I outlined a four-step AI adoption strategy for engineering teams. The first step — building a knowledge layer — is the one most teams skip, and the one that matters most. This ...
Source: DEV Community
In a previous post, I outlined a four-step AI adoption strategy for engineering teams. The first step — building a knowledge layer — is the one most teams skip, and the one that matters most. This post is the practical follow-up. How do you actually build that knowledge layer when you have 50+ engineers, hundreds of Confluence pages, thousands of Jira tickets, and dozens of GitHub repos? The problem: context fragmentation In any mid-to-large engineering org, knowledge lives in at least three places: Confluence — architecture decisions, runbooks, domain models, onboarding docs Jira — what's being built, why, by whom, and what's blocked GitHub — the code itself, PRs, reviews, comments, API contracts An engineer working on Service A needs to understand how Service B's API behaves, what the Confluence architecture doc says about the integration, and whether there's a Jira ticket in flight that changes the contract. Without that cross-service context, AI generates code that compiles but bre
