Start with the system, not the brand
An AI product is usually a stack: an interface, one or more models, tools, policies, retrieval systems and product logic. A model predicts or transforms representations; the surrounding system decides what context it sees and what actions it can take. This distinction explains why two products using related models can behave very differently.
Tokens and context are operating constraints
Models consume tokens rather than human “pages”. Context is a finite working area containing instructions, user input, retrieved evidence, conversation history and expected output. Long context is useful, but more context is not automatically better: irrelevant material can increase cost and make important evidence harder to use.
Treat fluent output as a proposal
Language quality is not evidence. Separate claims into facts, inferences and recommendations. Facts should be grounded in a source or a tool result when accuracy matters. For high-cost decisions, design a verification step before the model writes the final answer.
Choose AI where uncertainty is acceptable
AI is excellent at classification, transformation, drafting, extraction and fuzzy matching when you can verify the result. Deterministic rules remain better for hard constraints, arithmetic that must be exact, permissions and irreversible actions. The strongest systems combine both.
Your first useful workflow
Pick one repeated task. Define the input, expected output, failure cases and a small test set. Try the simplest model or tool that can solve it, measure quality and time saved, then decide whether automation is justified. That loop matters more than chasing the newest model.