Instruction: Craft a prompt that not only guides the language model to generate a novel algorithm for a defined problem but also to explain the reasoning behind the algorithm’s steps, its efficiency, and potential use cases. The problem context can be chosen by the candidate.
Context: This question tests the candidate's creative engineering skills, their ability to engage deeply with AI in generating innovative solutions, and their understanding of algorithm design principles. It requires the candidate to demonstrate how they can leverage the generative capabilities of AI for complex problem-solving and knowledge dissemination, ensuring the explanation is accessible to both technical and non-technical audiences.
I would constrain novelty so the model does not just produce impressive-sounding nonsense. A strong prompt would ask for assumptions, design goals, algorithm steps, complexity, and failure modes.
For example:
You are a research-oriented algorithm designer. Given the problem below, propose 2 candidate algorithms that are plausibly novel combinations or extensions of known ideas. For each one, explain the intuition, step-by-step method, time and space complexity, where it might outperform standard approaches, and what assumptions or weaknesses could break it. Do not claim novelty unless you explain what is new relative to common baselines.
That prompt is stronger because it rewards disciplined originality instead of empty invention.
What I always try to avoid is giving a process answer that sounds clean in theory but falls apart once the data, users, or production constraints get messy.
A weak answer asks for a "novel algorithm" with no requirement to justify assumptions, compare against baselines, or admit limitations.