Research & Innovation
Exploring the frontiers of AI-assisted software development
Our Research Focus
At Chaimantec, our primary research focus is on the integration of artificial intelligence into the software development process. We view AI not just as a tool, but as a performance enhancement that enables engineers to complete projects in unfamiliar technologies that would otherwise be challenging or impossible.
We're particularly interested in developing methodologies that prioritize code quality and long-term maintainability in AI-assisted development. This distinguishes our approach from "vibe coding" - a term coined by Andrej Karpathy referring to a development method that minimizes human involvement. While we recognize the speed benefits that AI can provide, our research emphasizes that proper engineering oversight is essential for production-grade software.
Key Research Areas:
- Quality-First AI Integration: Developing frameworks for ensuring high code quality standards in AI-assisted development
- AI as a Performance Multiplier: How AI tools can accelerate development velocity while maintaining engineering excellence
- Human-AI Collaboration: Optimal workflows for engineers working alongside AI assistants with an emphasis on code review and quality control
- Knowledge Transfer: Using AI to help engineers quickly gain competence in unfamiliar technologies while developing maintainable solutions
- Long-term Maintainability: Ensuring AI-generated code follows architectural best practices and remains maintainable over time
Our upcoming white paper will share detailed insights from our experiments and real-world projects using AI in software development workflows.
Key Research Insights
Accelerated Learning
Through our projects, we've discovered that engineers can quickly learn new technologies by observing AI-generated implementations. This "learning by watching" approach allows for rapid skill acquisition in unfamiliar domains.
Development Speed
AI assistance dramatically reduces development time for many types of projects. Tasks that would typically take weeks can often be completed in days, while maintaining high quality standards through proper human supervision.
Balance of Control
Our research indicates that finding the right balance between AI autonomy and human guidance is crucial. Too little oversight can lead to unexpected implementations, while too much control negates the benefits of AI assistance.
Skill Enhancement
Rather than replacing engineers, AI tools augment their capabilities, allowing them to work on more complex problems and in broader technology domains than would otherwise be possible.
Upcoming Research
AI in Software Development: Performance Enhancement for Engineers
Our forthcoming white paper will provide comprehensive insights into our research on AI-assisted software development, including practical guidelines, case studies, and quantitative results from our experiments.
Research Areas:
AI and Proprietary APIs
We're studying AI's capability to learn and use proprietary APIs. Even for companies not utilizing AI directly, their customers might use AI with proprietary APIs that weren't in the AI's original training. We're investigating how to provide and improve API documentation for AI consumption and evaluate its effectiveness. This includes examining whether instructions need to be repeated for AI, even if human readers find such repetition unnecessary.
Local LLMs for Code Quality
For organizations not embracing AI-generated code, we're exploring how Local LLMs can be used to enforce code quality. Engineers often resist following AI's suggestions, but codebase rules or "norms" must still be followed. We're building a system focused on enforcing coding standards within a codebase using Local LLMs instead of more capable cloud-based models, evaluating their effectiveness in this focused role.
Interested in receiving our white paper when it's published?
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