I started with linear congruential generators because that's the first thing I remembered from school, but then they pushed on distribution quality and seed management and I kind of ran out of steam.
Start by clarifying the requirements: what quality, speed, and security properties are needed? Then outline a design that balances simplicity, performance, and statistical quality, and discuss trade-offs between different PRNG algorithms.
Pro tip: Mention that for most applications, a simple xorshift or PCG algorithm is sufficient, but for cryptographic purposes, a CSPRNG like ChaCha20 is necessary—showing awareness of context and avoiding over-engineering.
Ask about the intended use: is it for simulations, games, or cryptography? Determine needed properties like period, speed, memory, and security.
Select between linear congruential generators (LCGs), xorshift, PCG, or cryptographic PRNGs based on requirements. Explain why the chosen type fits.
Define the state size, seeding mechanism, and output function. Ensure the state transition and output generation are efficient and avoid biases.
Discuss how to test randomness (e.g., Diehard tests, TestU01) and ensure the generator passes them for the intended use.
Compare performance, memory, and security trade-offs. Mention possible improvements like jump-ahead or parallel streams.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.