You are given a language model output string (text) and a list of source strings (sources). Your task is implemented in up to three progressive parts:
Part 1 – Count Matches: Return a list of match counts, one per source, indicating how many times each source phrase appears in the text. Matching is word-level (e.g., source 'network' does NOT match 'networking'; source 'blue' does NOT match 'blueprint').
Part 2 – Tag Matched Regions: Return the text with all matched source occurrences wrapped in <yellow></yellow> tags. Key constraints:
<yellow> block covering the union span. The prompt, as two candidates quote it, also merges matches that are merely adjacent (touching across a space); one candidate's unconfirmed preparation note says those stay separate. Default to merge and confirm with a three-word example if your prompt omits the word "adjacent."'The quick brown fox jumps over the lazy dog.', sources = ['quick brown', 'brown fox jumps'] → Output: 'The <yellow>quick brown fox jumps</yellow> over the lazy dog.'Part 3 – Add Citations: After each </yellow> closing tag, append bracketed source indices indicating which sources contributed to that highlight, e.g., <yellow>quick brown fox jumps</yellow>[0][1]. When multiple sources are cited for one merged highlight, sort the indices by descending frequency of that source across the entire text; ties broken by ascending source index. Example from a 3-part variant: <yellow>balabala</yellow>[1][0].
All characters are alphanumeric; words are separated by single spaces. Working correctness is prioritized over optimal time/space complexity.
Now wrap matched regions in <yellow></yellow> tags, merging overlapping or adjacent matches into a single tag block.
Append citation indices [source_index] after each closing </yellow> tag to indicate which sources are referenced. If multiple sources merged into one highlight, list all their indices sorted by descending global frequency, with ties broken by ascending source index. (when: After completing Part 2 (tagging))
What is the time and space complexity of your solution? What optimizations could you apply?
How would you handle word-boundary constraints so that 'blue' doesn't match inside 'blueprint'?
Should the citation frequency count be local to each highlight or global across the entire text? (when: Candidate treats citation count as local (per-highlight) rather than global)
| Approach | Notes |
|---|---|
| Trie-based word scanning | Build a Trie of source phrases split into words; scan the text word by word through the Trie to find all match ranges efficiently. More complex to implement but reduces redundant comparisons when many sources share prefixes. |
| Preprocess & extend overlapping phrases before lookup | First merge all overlapping/chainable source phrases into extended phrases, then do a single lookup pass. Simplifies the tagging step but requires a non-trivial preprocessing phase. |
Common mistakes: Treating citation frequency as local to each highlight rather than global across the entire text — at least one candidate was corrected on this mid-interview.; Doing character-level substring matching instead of word-level matching, causing sources like 'blue' to incorrectly match inside 'blueprint'.; Spending too much time explaining the approach upfront and running out of time to write the code — one candidate noted 'said too much at the start, time got tight at the end, ended up frantically coding without being able to talk through it.'; Writing a self-introduced 'optimization' that introduced a bug, then spending most of the session debugging it instead of finishing the solution.; Failing to handle overlapping sources correctly — one candidate noted getting the state transitions wrong in their single-pass scan approach, resulting in bugs the interviewer's test cases caught.; Not finishing Part 2 in time, only reaching Part 1 — one candidate reported only completing the first question.
Interviewer hints: Interviewers have their own test cases ready and run them against the candidate's code in the shared coding environment.; The interviewer corrected one candidate who used local frequency, explicitly saying citation counts should be global across the entire text.; The interviewer told one candidate upfront: 'Don't focus on the most efficient time/space complexity — focus on a working solution.'; One interviewer helped point out an edge case during the session, described as very nice and helpful once they understood the candidate's approach.; One interviewer initially could not follow the candidate's approach because it differed from the expected solution, went silent, but became more helpful once they understood it — though by then it was too late to debug.; The session is described as 'no bullshit' — interviewers go directly to the problem after a brief self-introduction, and the coding portion is roughly 45–60 minutes of pure coding with no separate BQ.
What passers do: Candidates who passed completed Part 1 (match counting) cleanly and used it as the foundation for Part 2 and Part 3, reusing the global frequency counts for citation ordering.; Successful candidates explicitly split text into word tokens and compared whole tokens to enforce word-boundary matching, rather than doing character-level substring search.; Candidates who passed verbalized their merge-interval approach early, found all match ranges first, sorted and merged them, then reconstructed the string — keeping the three steps clearly separated.; One candidate used a trie for preprocessing sources and a min-heap for interval merging, which impressed the interviewer even though the brute-force approach was accepted.; Passing candidates tracked which source indices contributed to each merged interval so they could correctly append multi-source citations after a single </yellow> tag.