GenAI for forward engineering is an emerging technique for modernizing legacy systems through AI-generated descriptions of legacy codebases. It introduces an explicit step focused on what the legacy code does (its specification) while deliberately hiding how it’s currently implemented. This relates to spec-driven development but is specifically applied to legacy modernization.
By generating and iterating on functional descriptions before rewriting the code, teams can use GenAI to surface hidden logic, dependencies and edge cases that might otherwise be overlooked. Emphasizing the problem space rather than the existing system also allows GenAI models to explore more creative and forward-looking solutions. The workflow follows a reverse-engineering → design/solutioning → forward-engineering loop, which enables both humans and AI agents to reason at a higher level before committing to an implementation.
At Thoughtworks, we’re seeing multiple teams apply this approach successfully to accelerate the rewrite of legacy systems. The goal isn’t to obscure implementation details entirely, but to introduce a temporary abstraction that helps teams and agents explore alternatives without being constrained by the current structure. This technique is showing promising results in producing cleaner, more maintainable and future-ready code while also reducing the time spent understanding existing implementations.
