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You mean seed the context with random information then ask it to ideate on something totally unrelated?

Like you want it to figure out how to get som information in front of potential customers in a cost effective way but first you give it articles on Witchita, Kansas, the curling iron, and List of Italian Brands?

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Yes, I havent seen research on this tactic specifically for language models but for generative image models you can find research thatindicates you get far more variety and diversity in the images returned when suffixing the context with "noisy context" such as random letters or something else unrelated that wont "confuse" it or make it go off the rails

That's how I understood it! Clever hack. You'll get more varied ideas by using many different seeds, than if you didn't use a seed and just asked the LLM in a fresh context.

Reminds of Freudian dream analysis.

The actual content of the dream is meaningless, lacks symbolism, but it prompts the patient to think deeply about personal feelings and memories that might otherwise not surface to conscious level.

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