The integration of AI tools, such as Claude Code, has significantly accelerated coding and requirements analysis processes. However, developers emphasize that the core challenge remains defining the correct system architecture rather than just generating code, as automated tools can produce technically functional systems that fail to meet actual business needs.
A case study involving an electronic archive project highlighted the risks of automated development without deep analysis. A previous contractor delivered a system that technically functioned but lacked required features and included unnecessary ones, as the developer failed to engage with the client's specific operational requirements.
Effective development now involves building an ecosystem around AI models, including custom rules, instructions, and tool integrations. For a medical event management system, the team had to challenge existing client regulations and simplify complex request structures to ensure the final product remained functional and logical for end-users.
Prototyping remains a critical stage for filtering out flawed hypotheses before full-scale development. By using interactive mockups, teams can identify potential issues in complex interfaces, such as filter management, which would be costly to fix after the system is built.
The author suggests that clients evaluating contractors should look for specific stages in proposals: dedicated analytical phases, pre-development prototyping, and post-launch support. A contractor willing to challenge a technical specification is often more likely to have fully understood the project requirements.
A separate study has demonstrated a method to reduce the security level of RSA encryption using classical computing power. The attack successfully compromised legacy 1024-bit keys within a few months using an academic cluster of processors.
