AI Experiment: AI-Powered Doc Review & Smart Suggestions
Reviewing documents — whether they’re marketing proposals, design rationales, or strategy drafts — is one of those tasks that looks simple but is rarely done well. Feedback often arrives fragmented: a comment on grammar here, a vague “this could be clearer” there, and occasionally a high-level suggestion that lacks specifics. As a result, authors spend more time interpreting feedback than actually improving the document.
That gap is what sparked this experiment.
I wanted to explore how AI could act as a first-pass reviewer for documents — not just catching surface-level issues, but understanding intent, structure, and clarity. Inspired by how humans review docs, I designed an AI-driven proposal review experience that reads through a document and flags suggestions across multiple dimensions such as readability, correctness, clarity, punctuation, and formatting — all in one place.
Instead of overwhelming users with raw edits, the system highlights specific phrases, explains why something might need attention, and groups feedback into clear categories. This allows writers to quickly scan what matters most, decide what to act on, and maintain ownership of the final decisions. The goal wasn’t to “auto-fix” documents, but to make feedback more transparent, structured, and even a little fun.
What makes this experiment interesting to me is how it mirrors real-world collaboration. The AI behaves less like a grammar checker and more like a thoughtful reviewer — pointing out imbalances, unclear phrasing, and structural inconsistencies while still respecting the author’s voice. It lowers the barrier to good feedback, especially in fast-paced environments where peers don’t always have time to review every draft in depth.
This experiment reinforced my belief that AI can meaningfully support creative and strategic work — not by replacing judgment, but by accelerating clarity and improving iteration quality.
WHAT’S NEXT
To take this further, I’d like to:
Allow users to choose the review tone (strict, friendly, or leadership-style).
Add support for different doc types like PRDs, marketing proposals, or design specs.
Enable before-and-after comparisons to show how a document evolves after applying suggestions.
At its core, this project explores how AI can make feedback feel less intimidating, more actionable, and far more scalable — while keeping humans firmly in control of the final outcome.