You can learn this.
One useful task at a time.

A language model learns patterns from data and generates text using its available context. Fluent wording does not establish truth. A model can produce a plausible detail that is unsupported or wrong. Check important statements against an appropriate source and keep uncertainty visible.

Your task, conversation, attached text, and supplied examples can influence the answer. There is a limit to how much a system can handle at once; older material may be omitted or summarized. Restate essential facts and constraints when a task changes. A long conversation is not a dependable record of your work. Keep your own source sheet.

A system may search files or the web before answering. This can provide relevant evidence, but retrieving a page does not make it current, applicable, or correctly interpreted. Open the cited source, check the date and scope, and compare the actual passage with the claim. A citation that does not support the claim is not verification.

A connected assistant may call software to search, calculate, edit, or send. Separate a proposed action from an authorized action and a successful result. Give a tool only the access it needs. For consequential actions, define the human approval point, inspect the result, and keep a receipt. An instruction to be careful does not replace access controls.

A prompt gives instructions and context for a task. Retrieval supplies selected material at answer time. Fine-tuning changes a model through additional training examples. Uploading a file into a conversation does not necessarily train a new model. For a small workflow, first clarify the task and test examples before assuming custom training is required.

A dependable workflow also needs an approved source, a user interface, data boundaries, tests, an owner, and a way to recover from errors. A stronger model does not fix an unclear customer need or an incorrect source document. Compare the full workflow with a reasonable manual alternative.

Use ordinary, ambiguous, missing-information, and failure cases. Decide what a good result looks like before running them. Count checking and rework as well as generation time. A small successful test supports a limited next step; it does not prove performance on every future input. Repeat relevant tests after changing the instructions or tool.

A realistic image or convincing voice is not evidence of who created it or whether an event occurred. Inspect the source, context, and independent confirmation. Ask permission before using someone’s identity or private media. Provide text descriptions and captions so essential information is available in more than one form.

Before supplying real information, check the provider’s current retention, training-use, sharing, and deletion settings and your organization’s rules. Removing a name may not remove identifying details. Use the Academy’s fictional material while learning. Keep passwords, access keys, and private client or student records out of public practice tools.

Test whether wording, assumptions, language, or access requirements exclude people. Ask affected people what is difficult and keep a non-AI route where appropriate. A useful output for one person is not proof that a service is accessible or fair for everyone.