The base chunking part felt manageable but the header-prepending requirement is where I started second-guessing myself.
Start by clarifying the requirements: max chunk size, whether to split on semantic boundaries (e.g., paragraphs, code blocks), and how to handle oversized atomic blocks. Then outline a two-pass algorithm: first parse the markdown into a hierarchical structure tracking headers, then greedily pack blocks into chunks while maintaining a stack of ancestor headers to prepend when a new chunk starts.
Pro tip: Mention that you'd treat headers as a stack and only include the minimal set of ancestors needed for context, avoiding redundant repetition of headers that are already at the top of the chunk. Also, discuss the trade-off between strict size limits and semantic coherence—sometimes exceeding the limit slightly is better than splitting a code block.
Ask about the maximum size unit (characters, bytes, tokens), whether chunks should be split at semantic boundaries, and how to handle content that exceeds the limit on its own (e.g., a large code block).
Use a markdown parser to tokenize the document into blocks (headings, paragraphs, lists, code blocks) and build a tree or flat list with header levels and ancestor relationships.
Iterate through blocks, maintaining a stack of current headers. When adding a block would exceed the limit, start a new chunk: prepend the current header stack (as markdown) before the block, then reset the stack to those headers.
If a single block exceeds the limit, decide whether to split it (e.g., by lines) or allow it to exceed; if splitting, ensure headers are repeated in each sub-chunk. Also handle documents with no headers or nested headers correctly.
Test with edge cases (deeply nested headers, large code blocks, empty sections). Discuss trade-offs: strict size vs. semantic coherence, performance of parsing vs. streaming, and whether to include header hierarchy in metadata instead of repeating.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.