AI & DATA

Pure LLM: Smart but frozen in time—answers from old training data, risks hallucinations on current events. RAG: LLM + real-time search—retrieves fresh facts from docs/web first, then generates accurate answers with fewer errors. LLM guesses from the past; RAG checks the present. Ideal for facts, support, research.

Agentic AI in 2025 is just a Meeseeks Box handed to Jerry. It spawns, it panics, it forgets the last five seconds, then screams “OOOH I CAN’T DO IT!” and dies in a cloud of failed KPIs. No memory, no learning, no chance. We’re not building agents. We’re mass-producing existential crises in code.

95% accurate AI can still miss most real problems. Use Accuracy only for balanced data. For real life (fraud, cancer, spam, self-driving), trust Precision, Recall, and F1 Score instead. One metric lies. Three tell the truth.

If your AI can’t confess its 27% chance of being wrong, it’s not intelligent—it’s a liar with a PhD. Expose uncertainty. Let users correct it. Or watch Sarah from Accounting name her cactus “Regret.”

AI speeds easy code +40%, legacy monoliths +5% (or -5% in COBOL hell). Stanford’s 100k-dev study: average win = 15-20%, mostly from boilerplate. Faster shipping = 10× rework on bad ideas. Real bottlenecks? Killer ideas + 6-week A/B tests. Verdict: Use AI as turbo-intern; keep customer calls sacred.