Common pitfalls when building generative AI applications

Chip Huyen · 16 Jan 2025 · huyenchip.com ↗

3 korrents from this piece

Chip Huyen did not write this page.

Every claim below was made in this piece, quoted word for word and numbered in the order the piece makes them, so you can read it there rather than take our word for it. The sentence above each quote is our reading of the claim, not their wording. Each quote was checked against a stored copy of the page at build time; where the two differ, the quote is the fact.

Our reading
As AI products converge on the same underlying models, product design becomes the real differentiator, not the model itself.

Their wordsBecause everyone uses the same models nowadays, the AI components of AI products are similar, and the differentiation is product.
Our reading
Building a working AI demo is far easier than turning it into a reliable production product.

Their wordsThis is perhaps one of the first painful lessons anyone who has built an AI product quickly learns. It's easy to build a demo, but hard to build a product.
Our reading
AI judges used to evaluate AI outputs need ongoing validation and iteration, just like any other AI system.

Their wordsAI judges must be evaluated and iterated over time, just like all other AI applications.