Artificial intelligence is no longer just about choosing the latest LLM. More teams are realizing that the real challenge is building AI products that are reliable, secure, scalable, and actually useful in production.
I've noticed many engineering teams spend weeks comparing models like GPT, Claude, Gemini, or open-source alternatives, but much less time discussing questions like:
How do you monitor AI applications after deployment?
What does a good evaluation pipeline look like?
How do you handle hallucinations in production?
