LLMs for Business — what they can and can't do

An LLM is a statistical model of language. It predicts the next word based on the previous ones, trained on enormous amounts of text. That's it. No consciousness, no 'understanding' in the human sense — but an extraordinary competence for transforming text.

What LLMs are really good at: summarizing (long report → 3 bullets), extracting (invoice → structured data), transforming (technical paragraph → customer-friendly email), classifying (ticket → urgent/normal/low), drafting (job posting, marketing email, basic code), translating. When the task is 'text in → text out', they shine.

What LLMs are NOT good at: exact math (ask them to calculate 8847 × 2316, not guaranteed), real-time information (they only know what was in their training set, often 6-18 months old), long-chain reasoning without tools (prove a theorem, solve a 15-step logic problem), respecting confidentiality (they don't understand that this data is sensitive unless you force them through the prompt/guardrails).

**Myths to dispel**: 'AI will replace everyone' → no, AI replaces **tasks**, not jobs. A lawyer won't disappear but 30% of their time on standard drafting can be automated. 'AI is always right' → no, LLMs **hallucinate** (invent plausible but wrong facts) ~5-20% of the time depending on the topic. 'Fine-tuning = making the model smarter' → no, it adjusts style/format, not raw intelligence.

The right question before any project: what is the real cost of an error? If a summary misses a nuance, that's OK. If a medical diagnosis is wrong, that's not OK. Match the use case to LLM reliability — high-risk use cases always need human-in-the-loop.