Two AI reviews agreeing is not two reviews: how I learned to test claims before adopting them
The article discusses the author's experience with two AI reviews yielding identical scores and criticisms. Initially, the author considers this convergence as validation of their work but soon realizes it reflects the overlap in the training data of the AI models rather than an objective assessment. This leads to a broader reflection on the importance of independent verification in evaluating claims made by AI systems.
- ▪The author submitted their work to two AI reviews and received the same score and similar criticisms.
- ▪Upon reflection, the author recognized that the convergence of the AI reviews was due to the overlap in their training data.
- ▪This experience highlighted the need for independent verification when assessing claims made by AI.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3897818) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Michel Faure Posted on May 24 • Originally published at dev.to Two AI reviews agreeing is not two reviews: how I learned to test claims before adopting them #claudecode #ai #webdev #productivity My ERP with Claude Code (33 Part Series) 1 How much are 91,000 lines produced with Claude Code actually worth? 2 Supabase RLS in production: four traps that silence your queries ... 29 more parts...
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
