Summary
- Following a performance issue discovery, the team needed comprehensive guidance on building more resilient backend services to prevent entire classes of problems
- Traditional approaches require senior engineer time for code reviews, architectural guidance, mentoring sessions, and establishing team-wide best practices
- Resolve AI provided detailed engineering mentorship covering resilience patterns, code quality tools, testing strategies, and architectural best practices with specific implementation examples
- Delivered scalable engineering knowledge that elevates the entire team's capabilities rather than just solving immediate problems
“Asked Resolve AI for specific guidance on building more resilient backend services. Instead of generic advice, it gave me comprehensive architectural recommendations with actual code patterns, tool suggestions, and testing strategies. It's like having a principal engineer review our entire approach and provide detailed mentorship, but available to every developer on the team.”
What was the need?
After identifying and fixing the ad service performance issue, Spiros wanted to ensure the team wouldn't introduce similar problems in the future. This required more than just fixing the immediate code - it needed comprehensive guidance on building resilient backend services, establishing better development practices, and scaling engineering knowledge across the team.
Traditionally, this type of architectural mentorship requires significant senior engineer time: conducting code review training, establishing team standards, researching and recommending tools, creating testing guidelines, and providing ongoing mentorship. For growing teams, scaling this knowledge consistently across all developers becomes a significant challenge.
How did Resolve AI provide engineering mentorship?
Resolve AI identified the root cause of sluggishness in the UI to a recent code change. Reasoning like seasoned engineer and seamlessly working with Cursor, Resolve AI geenerated a PR to address the performance issue
Beyond the fix, Resolve AI approached this as a comprehensive engineering mentorship session, providing detailed guidance across multiple domains. Rather than generic advice about "avoiding blocking calls," it provided specific architectural patterns, tool recommendations, and implementation strategies that address the root causes of similar issues.
The guidance covered immediate tactical improvements (avoiding unnecessary blocking calls, implementing timeouts and circuit breakers) and strategic architectural patterns (dependency injection, interface-based design, proper separation of concerns). Each recommendation included specific tools (Resilience4j, SonarQube, Error Prone) and implementation examples relevant to the existing Java/Spring Boot stack.
*Resolve AI provided comprehensive architectural guidance covering multiple engineering domains with specific implementation patterns.*
*Detailed code patterns and architectural examples provided immediately actionable guidance for the development team.*
The mentorship went beyond identifying problems to teaching solution patterns. Resolve AI provided specific code refactoring examples showing how to implement circuit breakers, handle dependency injection properly, and structure services for better testability and resilience. The examples weren't theoretical - they were directly applicable to the team's existing AdService implementation.
The guidance included process improvements that scale across the engineering organization: static analysis rules to catch similar issues during development, code review checklists to ensure consistent evaluation, and testing strategies to validate resilience patterns under load.
*Comprehensive approach covering development practices, tooling, testing, and organizational processes to prevent entire classes of issues.*
Resolve AI synthesized engineering best practices into a complete improvement plan. The recommendations spanned the entire development lifecycle: preventing issues during coding (linting rules, static analysis), catching them during review (PR checklists), validating them during testing (integration and load tests), and monitoring them in production (metrics and observability).
This represented the kind of comprehensive architectural review and mentorship typically available only from senior engineers or external consultants - but delivered immediately and available to every team member.
What was the impact?
- Provided immediately actionable guidance with specific code patterns, tool recommendations, and implementation strategies rather than abstract architectural advice
- Established comprehensive development practices covering coding standards, review processes, testing strategies, and production monitoring
- Prevented entire classes of future issues by teaching resilience patterns and architectural best practices rather than just fixing immediate problems
- Accelerated team capability development by providing principal engineer-level guidance embedded in the normal development workflow
