Build a 30-Line Provenance Log for AI-Generated Code

A developer proposes a 30-line provenance log for AI-generated code using an append-only JSONL file. The log records task, constraints, model, commits, verification, and human decisions without exposing secrets. This creates a searchable chain from intent to evidence, making generated code replaceable while preserving decisions.

“Control the ideas, not the code” is trending today. For a small team, the practical question is how to preserve decisions when code becomes cheap to regenerate. Use an append-only JSONL log. It should record the task, constraints, model or tool, input commit, accepted output commit, verification command, and human decision—never raw secrets or an entire private prompt. js import { appendFileSync } from "node:fs"; import { execFileSync } from "node:child process"; const git = ...args = execFileSync "git", args, { encoding: "utf8" } .trim ; const entry = { at: new Date .toISOString , task: process.argv 2 , constraints: process.argv 3 .split "," , input: git "rev-parse", "HEAD" , verify: process.argv 4 , decision: "pending" }; appendFileSync ".provenance.jsonl", JSON.stringify entry + "\n" ; Commit the log beside an architecture decision record. After review, add a second event containing the output commit, test result, reviewer, and accept/reject reason. Events are safer than rewriting history because retries and reversals remain visible. This does not prove authorship, correctness, or license compatibility. It creates a searchable chain from intent to evidence. That chain lets you replace a model, regenerate an implementation, or explain why a strange constraint exists six months later. The durable asset is not a pile of generated lines. It is the verified intent that makes those lines replaceable.