Research4d ago
Avoid Common Generative AI Pitfalls
Generative AI isn't always needed. Many teams pitch it for tasks like energy optimization, only to find it adds complexity. Chip Huyen warns against over‑engineering and ignoring data quality, highlighting common missteps. Practitioners should audit use cases, keep solutions simple, focus on real value, and monitor outcomes before deploying generative models.