Practical prompting, backed by research

Ask an LLM to solve a business problem and you'll get a mashup of Medium posts and Hacker News threads. That's not creativity; that's averaging the training data. Universe of Thoughts (UoT) lets you generate unconventional solutions for ill-defined problems (business strategy, innovation, product design). The trick: the model doesn't look for a solution inside the familiar domain but steps outside it: it finds structurally similar problems from other fields, takes their solutions apart into components, and plugs them into your problem. For maximum creativity, it changes the rules of the problem itself.

The finding: An LLM gets stuck on the first strategy it finds and drags it all the way to the end — even when the intuition is wrong. It's a greedy approach: the model doesn't explore alternatives, it commits to its first idea. DIP lets you produce a high-quality plan by synthesizing several draft approaches — instead of a single strategy or expensive multi-sampling. The trick: use the LLM's inductive capacity — its ability to synthesize patterns from examples. The model creates 5-7 different approaches to the task, expands each into a plan, then induces the final plan from all the drafts. 2-3x cheaper than Self-Consistency at comparable quality.