The seed-based determinism part is straightforward enough, but the 'no repeated sampling' constraint is what makes this interesting.
Start by clarifying requirements and constraints, then propose a deterministic algorithm that maps a seed to unique combinations without retries. Use combinatorial ranking (e.g., factoradic or mixed-radix) to directly index into the combination space, and validate N against the total possible combinations upfront.
Pro tip: Mention that you would use a seeded PRNG (like xorshift or Mersenne Twister) for deterministic shuffling, but avoid modulo bias by using rejection sampling or a permutation-based approach. Also, discuss how to handle large N efficiently with streaming or lazy generation.
Ask about the expected size of N, the number of traits and values, and whether the output order matters. Confirm that determinism means the same seed and config always produce the same set of NFTs.
Calculate the Cartesian product of all trait values to get the total possible unique combinations. If N exceeds this total, return an error immediately.
Use a combinatorial number system (e.g., factoradic) to map a seed-derived index to a unique combination without collision. Alternatively, generate a deterministic permutation of all combinations and take the first N.
For each of the N indices, decode the index into trait values using mixed-radix conversion, then assemble the JSON metadata. Ensure the process is efficient and scalable.
Address performance for large combination spaces, memory usage, and how to handle seed changes. Mention potential biases and how to avoid them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.