The phrase “AI developer” can describe two related activities: building applications that use AI, and using AI tools to develop software. Many engineers do both. In either case, the work extends from understanding the user’s problem to operating the result.
A prompt is one part of that work. The surrounding application still needs clear requirements, reliable data access, understandable interfaces, and evidence that it behaves as intended.
Own the application around the model
An AI feature connects to ordinary software concerns: identity, authorization, APIs, databases, deployment, and failure handling. The engineer needs to understand where the model’s output enters the application and which decisions are enforced by code.
For example, an assistant that drafts an invoice summary still needs a reliable way to locate the correct invoice and preserve its access rules. Product usefulness depends on that surrounding behavior as well as on the generated summary.
Use coding assistants with repository context
Provide the task, relevant files, acceptance criteria, and established conventions. GitHub documents repository instructions as a way to give Copilot recurring project context. Keep such instructions specific enough to guide a real implementation decision.[1]
Useful guidance might identify the approved data-access layer, where integration tests live, and how to run them. Keep credentials and sensitive records out of instruction files. Update the guidance when the project changes so it does not send the assistant toward obsolete patterns.
Review the change and its evidence
GitHub notes that coding agents and AI code review can produce incorrect suggestions, miss defects, or raise false positives. Generated work therefore needs human review and appropriate testing.[2]
Read the diff in the context of the requirement. Confirm that the agent did not silently change an interface, weaken an access check, or solve only the example in the prompt. Ask what was actually executed and inspect the result, rather than relying on a confident completion message.
Build an evaluation habit for AI features
For a model-powered feature, create examples that represent successful behavior and meaningful failures. A document assistant needs cases with missing evidence; a tool-using assistant needs cases where the tool fails or the requested action is outside its scope.
Keep evaluation criteria understandable to the people who own the workflow. A technically valid response can still be unhelpful if it omits the detail the reviewer needs. Combine automated checks with review of representative outputs.
- Define the user’s task and the boundaries of the feature.
- Record the context and configuration used for a result.
- Inspect errors and reviewer corrections as product feedback.
- Compare changes against a stable set of important examples.
Develop breadth through one complete project
A useful learning project connects a user interface, an API, a small authorized knowledge collection, and an evaluation set. Take it through deployment and diagnose a real failure. That experience reveals the relationship between product decisions, data quality, and application behavior.
When assessing an AI developer, look for an explanation of the design, tradeoffs, verification, and operational limits. The most useful evidence is a project the engineer can reason about and improve after the initial demonstration.
PUT IT INTO PRACTICE
AI tools can participate in implementation, but engineering ownership still covers the problem, the boundaries, the evidence, and the operation of the finished application.