How to Build Merkle Trees and Inclusion Proofs in Python From Scratch

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A developer building AI retrieval systems explored how to cryptographically prove that a specific source document was included in the set a model processed. The solution demonstrated is a Merkle tree, a binary hash structure where each internal node is the hash of its two children, producing a single root hash that acts as a fingerprint for the entire dataset. The tutorial walks through implementing a Merkle tree in Python from scratch, covering leaf hashing, tree construction, and generating compact inclusion proofs using only O(log n) sibling hashes. Two important correctness considerations are addressed: domain separation using distinct prefixes for leaf and internal node hashes to prevent second-preimage attacks, and deterministic handling of odd-numbered tree levels. Beyond AI provenance, the technique applies broadly to transaction logs, file backups, and certificate transparency systems.

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