The first question is whether the information is already publicly available. If it showed up in a search engine result, would it reveal anything about your company or a client? Also, confirm your company’s AI policy. Some organizations have approved enterprise tools specifically for internal or sensitive data.
Most likely. With an approved enterprise or government cloud tool, you can generally work with company-specific data, internal documents, and even customer information that would be off-limits with a public AI subscription. The key difference with these architectures is data residency and access controls: your information stays within a secured environment rather than potentially being used for model training or exposed externally. Even so, best practice is to confirm your company’s AI policy on what can be shared where.
The goal isn’t to redo the work, but you’re accountable for the output as if you produced it yourself. Verify it enough that you can stand behind it.
Clearly describe your problem and your initial hypothesis for what the analysis should determine or produce. Upload relevant documents, like spec sheets, datasheets, and provide the model with a specific direction. A targeted request like “Characterize the relationship between valve position and pressure” will outperform generic requests like “analyze this and tell me what you see.”
They give the AI actionable information to use in your workflow. For example, when building wrappers or drivers, add descriptive error codes so that when something fails, you can feed that output directly back to the model and get a specific diagnosis rather than guesswork.
Provide detailed context under both conditions. Start with a clear problem description and your initial thinking, then ask the model to ask follow-up questions. That conversation builds context naturally. For ongoing projects, summarize what you’ve covered into a document and add it to your project context so it carries forward.