From: aidotengineer
The fourth pattern of AI-native development focuses on capturing and organizing knowledge derived from the development process [00:10:31]. This ensures that past problems are not repeated and valuable insights are retained [00:10:39].
Sources of Knowledge
Knowledge can be derived from various aspects of the software development lifecycle:
Production Issues
Learning from experiences in production is crucial. Information gathered from production environments can be used to inform and improve code [00:10:49].
Incident Response
Analysis of incidents provides valuable lessons on what failed and what practices or technologies to avoid or improve upon [00:10:57].
Codebase as Lessons
Existing code can be transformed into lessons, which is particularly useful for reducing the onboarding time for new team members or preserving knowledge when someone leaves [00:11:11]. Much knowledge is typically lost in these transitions [00:11:22].
Feature Memory and Decisions
It’s common for features to be tried, dismissed, and then re-attempted later [00:11:34]. By maintaining a “feature memory,” teams can track past decisions and avoid redoing work or repeating mistakes. Decisions, often scattered across tickets or architecture diagrams, can be centrally captured as organized knowledge [00:11:47].
AI’s Role in Knowledge Capture
AI can facilitate the capture of knowledge directly within the workflow [00:12:05]. As developers chat and code, an AI agent can identify and prompt the saving of important information as knowledge [00:12:09]. This creates a beneficial loop where both humans and AI learn concurrently, fostering continuous knowledge retention [00:12:15].
“when we actually do this more in the flow we we chat we we code and all of a sudden the agent says I think this is important let’s save this as knowledge we kind of get a very beneficial loop I both for actually the the human and both for the eye to kind of learn at the same time and keeping that like knowledge” [00:12:05]
Benefits of Organized Knowledge
Organized knowledge serves multiple purposes:
- Assisting Others: It helps other team members with their questions [00:12:26].
- Improving Solutions: It contributes to improving the coding of solutions [00:12:28].
This pattern, along with others, demonstrates that AI’s impact on development workflow extends beyond mere coding assistance, moving developers into roles traditionally associated with operations, QA, architecture, product ownership, and data engineering [00:12:39]. This shift allows developers to act more like senior developers who engage in broader activities beyond just writing code [00:13:05].