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Manual AI Workflow

A two-phase workflow for environments where direct API access is not possible: air-gapped networks, regulated environments with strict egress controls, or teams that must route AI interactions through a supervised chat interface.

The workflow produces the same CSV output as API AI enrichment. The only difference is that the AI interaction is performed manually by an operator between the two phases.

Phase 1 — Prepare

Generate prompt files for every test class in the scan roots.

./methodatlas -manual-prepare ./work ./responses src/test/java

For each discovered test class MethodAtlas writes:

  • A work file in ./work/ containing the full AI prompt (taxonomy + method list + class source).
  • An empty response placeholder in ./responses/ that the operator will fill in after interacting with the AI.

No CSV is produced during Phase 1.

Between phases — operator steps

For each work file in ./work/:

  1. Open the file and locate the AI PROMPT block.
  2. Paste the prompt into your AI chat interface.
  3. Copy the AI's JSON response.
  4. Save it into the corresponding .response.txt file in ./responses/.

The response file may contain free-form prose around the JSON (for example if you copied the entire chat reply verbatim). MethodAtlas extracts the first JSON object it finds and ignores any surrounding text.

Phase 2 — Consume

Read the filled response files and emit the enriched CSV.

./methodatlas -manual-consume ./work ./responses src/test/java

Classes whose response file is absent or empty are emitted with blank AI columns; the scan does not fail.

Work and response directories

The two directory arguments may point to the same path when a single working directory is sufficient.

Combining with source write-back

After Phase 2, add -apply-tags to insert AI-generated annotations directly into the source files instead of writing a CSV:

./methodatlas -manual-consume ./work ./responses \
  -apply-tags src/test/java

See Source Write-back for details and caveats.