Built calibration-first annotation flow scaffold

• Day: 2026-06-28
• Time: 12:10 to 12:50
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Annotation, Schema-Design, Promptflow, Validation, Workflow

Description

Session Goal

Design a calibration-first, reproducible annotation workflow for the development finance project screening pipeline, with emphasis on schema alignment, validator behavior, and compatibility with the existing wrapper.

Key Activities

• Reviewed guidance to delay full-scale annotation and instead run a calibration smoke test using AidData samples.
• Defined a reproducible MVP package for contract calibration: sample set, schema, prompt, flow scaffold, validator, and a short audit of outputs.
• Refined schema design recommendations to mirror an OpenAI-style structure exactly, using string enums for status fields.
• Separated concerns between schema and validation: logical consistency checks should live in the validator rather than being over-constrained in the schema.
• Identified a compatibility constraint in the legacy template/wrapper path:
  • flow.dag.yaml consumes flat ${inputs...} values.
  • run.yml performs column_mapping from ${data...}.
  • The wrapper still expects the function name parsed_message, so the schema/function naming must remain compatible or be patched.
• Planned a PromptFlow wiring approach with Jinja prompt templating, DAG/run configuration, and staged smoke testing.

Achievements

• Clarified the operational strategy: use a small calibration sample first, not final labeling.
• Established the minimum reproducible deliverable for one-hour setup and validation.
• Resolved a key implementation risk by documenting the parsed_message naming dependency in the wrapper.
• Produced a concrete integration map for the old template and the new screening wrapper.

Pending Tasks

• Build the actual folder scaffold and wire the PromptFlow DAG/run files.
• Implement or update the validator to accept string labels and enforce logical consistency.
• Prepare the AidData calibration sample and run the smoke test.
• Decide whether to patch the wrapper or preserve the parsed_message contract end-to-end.
• Communicate the implementation summary and next steps to Eric.

Evidence

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