When working with language models like ChatGPT, Claude, or Gemini, it’s common for generated ideas to end up sounding very similar. Whether you need a name for a podcast, a Wi-Fi network, or a startup, LLMs tend to produce predictable and unoriginal lists. However, there is a two-stage prompting technique, backed by a Wharton School study, that can extract up to 100 truly diverse and unique ideas. The method is simple but requires discipline: first, ask the assistant to generate a large number of concepts with no quality filter; then, ask it to review the list to maximize diversity, removing redundancies and replacing them with completely different approaches.
The first stage focuses on raw quantity. The prompt should explicitly request 100 ideas, numbered, without explanations or descriptions. It should allow obvious, bad, weird, and half-baked fragments. The goal is to saturate the possibility space, forcing the model to explore unusual combinations. For example, if the aim is to name a coffee shop, the initial responses are often predictable: “The Daily Grind,” “Brewed Awakening,” “Central Perk,” etc. But as you get to the last thirty or forty names, more daring concepts emerge such as “Warm Noise,” “Morning Object,” or “Borrowed Sugar.” The Wharton study confirms that the most interesting ideas often appear at the end of the list, when the model is forced to leave the well-trodden paths.
Once we have the initial list of 100 ideas, the second stage arrives: diversification. We tell the assistant to review the list and, whenever it finds two or more ideas based on the same underlying concept, keep the best one and replace the others with new proposals covering completely different angles. The goal is that no idea shares the same conceptual base, even if the wording differs. If our list includes “Morning Cup,” “Morning Brew,” and “Daybreak Coffee,” all three clearly point to the same morning concept. The assistant must keep the strongest one and replace the others with ideas exploring, for example, the concept of “public space,” “frozen time,” or “casual encounter.” After this second pass, the list becomes genuinely diverse: names like “Public Living Room,” “Blue Hour,” “Localhost,” or “Borrowed Sugar” offer very different perspectives on what a coffee shop can represent.
Optionally, a third stage can be added to filter by quality: ask the model to select the 10 most interesting ideas from the diversified list. But I recommend personally reviewing all 100 final ideas, because hidden gems often appear in strange guises that the algorithm might prematurely discard. This process works not only for brand names; it is equally effective for generating product features, marketing strategies, technical function names, or problem-solving approaches.
In the business and technology realm, the ability to obtain divergent ideas is crucial. A software development company, for instance, can apply this technique to explore possible architectures, internal service names, or innovative functionalities. At Q2BSTUDIO, a company specialized in custom software applications, we use advanced prompting methods to boost creativity in our teams. By combining this technique with expertise in artificial intelligence, we ensure that models generate proposals that break away from usual patterns and provide true competitive differentiation.
Diverse idea generation has a direct impact on areas like cybersecurity, where finding names for threats, protocols, or defense tools with original perspectives can make a difference. Also in the cloud, when designing services on AWS or Azure, the ability to think of unusual combinations of resources helps optimize costs and performance. Q2BSTUDIO integrates cloud services on AWS and Azure in its projects, and applies AI-assisted brainstorming techniques to identify architectural patterns that no competitor has considered. In Business Intelligence, using Power BI, generating alternative metrics and visualizations can reveal hidden insights in data. Finally, AI agents greatly benefit from this diversity: an agent trained to generate varied ideas can help product teams explore paths that would otherwise go unnoticed.
The two-stage method is not a magic recipe, but it is a structured way to break the creative inertia of models. By forcing quantity first and then diversity, we push the LLM out of its semantic comfort zones. Instead of receiving ten variations of the same idea, we get a hundred distinct paths to evaluate, combine, and refine. It is especially useful when you need a name for a new product, a slogan, a campaign, or even a data structure. The key is not to settle for the first ten suggestions and to apply the second stage rigorously.
In a world where originality is increasingly valued, this technique offers a tangible advantage. Companies that master the art of prompting not only get better responses, but also accelerate innovation cycles. Q2BSTUDIO, with its focus on custom software development, AI agents, cybersecurity, and cloud, demonstrates that combining technology with well-directed creativity can transform generic ideas into unique solutions. Next time you need ideas, remember: ask for a hundred, then diversify, then choose wisely. The result will be a portfolio of concepts that truly stand out from everything else.
