← Anthropic Interview Insights
Clarify the problem constraints (vocabulary size, string length, character set) and discuss the naive O(n * L * V) approach, then optimize using a trie for O(n * L) time. Walk through the algorithm step-by-step, handle edge cases, and analyze time/space complexity.
Pro tip: Mention that a trie can be built once and reused for multiple strings, and that the greedy approach may not yield the globally optimal tokenization—this shows awareness of trade-offs beyond the immediate problem.
Ask about vocabulary size, maximum token length, input string length, and character set. Confirm that the greedy algorithm is required (not optimal tokenization) and that -1 is emitted for unmatched characters.
Propose a trie (prefix tree) built from the vocabulary. At each position, traverse the trie to find the longest matching token; if none, emit -1 and advance by one.
Write clean code with helper functions for trie construction and matching. Test with edge cases: empty string, no matches, overlapping tokens, and tokens that are prefixes of others.
State time complexity O(n * L) where L is max token length, and space O(V * L) for the trie. Discuss alternatives like sorting vocabulary by length or using a hash set for each length.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the tokenizer's input/output contract and the meaning of -1 (unknown token). Then propose a single-pass algorithm that tracks whether the previous output was -1, appending -1 only when transitioning from a known token to an unknown one, and handle edge cases like leading/trailing unknowns.
Pro tip: Mention that merging unknowns can be done in-place or with a new list, and discuss whether the tokenizer should preserve the count of unknowns for debugging or metrics. This shows awareness of production trade-offs beyond the basic algorithm.
Ask whether -1 represents a single unknown token or a placeholder for any unknown character, and confirm expected behavior for consecutive unknowns at the start, middle, or end of input.
Use a single pass with a boolean flag (e.g., prev_was_unknown) to decide whether to append -1. Alternatively, use a stack or list and merge after tokenization.
Write clean code with clear variable names, and test with cases like all unknowns, alternating known/unknown, and empty input.
State O(n) time and O(1) extra space (if in-place) or O(n) space (if new list). Discuss whether merging should happen during tokenization or as a post-processing step.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem context—likely string matching or dynamic programming where the inner loop iterates over a small vocabulary. Then, explain how to optimize by precomputing or indexing vocabulary entries, and highlight the key insight: the maximum useful match length is bounded by the longest vocabulary word, so you can cap the inner loop at that length.
Pro tip: Mention that this optimization is crucial in production systems like tokenizers or spell checkers, and that you'd validate the trade-off between precomputation memory and runtime speed. Also, note that if the vocabulary is truly tiny, a simple linear scan with early termination may suffice, avoiding over-engineering.
Ask or state the context: what is the input, what is the vocabulary, and what is the inner loop doing? Confirm that vocabulary size is small relative to input length.
Explain that the naive inner loop checks every vocabulary word at each position, leading to O(n * V) time. Since V is small but n is large, the constant factor matters.
The longest vocabulary word has length L. Any match starting at position i cannot extend beyond i+L-1. So the inner loop only needs to consider substrings up to length L, reducing work to O(n * L).
Use a trie or hash set of vocabulary words to check matches in O(L) time per position, or precompute a set of all substrings up to length L. This avoids iterating over the entire vocabulary.
Mention that if L is also small, the optimization is straightforward. If not, consider more advanced structures like Aho-Corasick. Also, note that early termination (breaking when no prefix matches) can further speed up.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.