While detailed prompts are useful, starting with simple, open-ended prompts can unlock more creative and strategic responses from AI models. Experimenting with different levels of prompt detail across various models often yields surprising and superior results.

Avoid writing long, paragraph-style prompts from the start as they are difficult to troubleshoot. Instead, begin with a condensed, 'boiled down' prompt containing only core elements. This establishes a working baseline, making it easier to iterate and add details incrementally.

With models like Gemini 3, the key skill is shifting from crafting hyper-specific, constrained prompts to making ambitious, multi-faceted requests. Users trained on older models tend to pare down their asks, but the latest AIs are 'pent up with creative capability' and yield better results from bigger challenges.

For subjective tasks, refining instructions has diminishing returns. The most effective way to improve AI performance is to provide it with a set of high-quality examples of the desired output. A library of five great examples is more powerful than a perfectly crafted prompt.

Instead of manually refining prompts, a superior workflow uses a model strong in text and logic (like Claude) to generate a highly structured, "OCD-level" prompt. This output can then be fed into a specialized model (like an image generator) to achieve far more precise and desirable results, leveraging the distinct strengths of each AI.

The traditional lever of `temperature` for controlling model creativity has been superseded in modern reasoning models, where it's often fixed. The new critical parameter is the "thinking budget"—the amount of reasoning tokens a model can use before responding. A larger budget allows for more internal review and higher-quality outputs.

When an AI tool automatically gathers rich, timely context from external sources, user prompts can be remarkably short and simple. The tool handles the heavy lifting of providing background information, allowing the user to make direct, concise requests without extensive prompt engineering.

The best prompts strike a balance between providing enough specific information (e.g., "include code excerpts") and not over-constraining the model. Adding a phrase like "whatever is needed to give me maximum context" gives the AI an "out" to use its own judgment and provide additional, helpful information you didn't ask for.

Instead of using absolute negatives like "never do X," explain the underlying reason you want to avoid X. This gives the model flexibility. For example, rather than a hard character limit, explaining the goal is a single tweet but allowing a thread if necessary gives the AI more freedom to create a better output.