Should I use consistent PascalCase and camelCase names when prompting AI?

Use exact identifiers when you already have a specific type, table, column, endpoint, or component name that must be preserved. Otherwise, just explain the behavior in normal language. The most effective prompts tend to include precise, testable requirements, constraints, examples, wireframes, or mockups—similar to how you would brief a capable junior developer.

Consistency is useful once a concept has been established. If `CarScrollView` always refers to the same component, the AI has less ambiguity to resolve and is less likely to invent alternate names. The savings and accuracy boost probably won’t be dramatic, though—the bigger improvement comes from giving clear requirements and correcting bad assumptions.

Natural language generally works well as long as you provide enough context. You don’t need to name every component in advance, and the model can usually settle on sensible naming conventions itself. One thing to watch for is overloaded terminology: if you call something an MVP or “phase one,” it may start adding those words to unrelated names throughout the code.

Put durable conventions in project documentation or instruction files rather than repeating them in every prompt. A file describing the architecture, naming rules, design system, and expected workflow gives the AI stable context and keeps individual prompts shorter. Natural language is fine there too; identifiers are mainly valuable when they map to real code concepts.

Precision matters more than whether the wording is PascalCase or conversational. Saying “use a `HashSet` for unique values” is much better than saying “use a thing that stores stuff,” because the technical term carries concrete constraints. Establish names when they matter, but don’t force artificial names for ideas that haven’t become actual parts of the design yet.