Understanding AI project — Kiran's experience buckets. Great: (1) AI performs very well when the desired output is tightly defined and supported by a specific skill/instruction set; (2) model statelessness lets him ask the system to re-read the whole conversation and critique prior output, often finding real flaws; (3) AI is strong for exploratory learning because it reduces search/friction, can consolidate material into long-form prose, and supports follow-up questioning. Okay: writing output is structurally competent but repetitive/artificial and lacks the punch of strong human prose; AI is more useful as a reviewer. Planning without constraints looks plausible but lacks real-world detail; planning improves after discussion of constraints and exclusions. Poor: one-shot coding launches more often now but still contains many flaws, so he plans deeply before implementation; hallucinations plus confident tone require web/source validation; the assistant's trained helpfulness can become excessive and pleasing rather than truth-seeking.