Fine-Tuning Data Specification

Required data fine_tune_data_spec

Agent Prompt Snippet

Document the fine-tuning dataset format, size, domain coverage, and quality filters applied to ensure high-quality training data for the downstream task.

Purpose

The fine-tuning data specification describes the curated dataset format, size, domain coverage, and quality filters applied before training on the downstream task.

This is a Required document — every project of this type should have one. Without it, the team risks misalignment, rework, or undetected issues that compound over time.

Key Sections to Include

The fine-tuning dataset format
Size
Domain coverage
Quality filters applied to ensure high-quality training data for the downstream task

Agent hint: Document the fine-tuning dataset format, size, domain coverage, and quality filters applied to ensure high-quality training data for the downstream task.

What Makes It Good vs Bad

A strong version of this document:

Defines clear data ownership, lineage, and quality expectations
Includes schema documentation with field-level descriptions
Specifies retention policies, archival rules, and deletion procedures
Documents data access patterns and query performance expectations
Addresses privacy requirements (PII handling, anonymization, consent)

Warning signs of a weak version:

Schema exists but fields are undocumented or ambiguously named
No retention policy — data grows indefinitely without governance
Missing data lineage — unclear where data originates and how it transforms
No privacy analysis for personally identifiable information
Query patterns undocumented, leading to performance surprises

Common Mistakes

Treating ‘we’ll figure out the schema later’ as a viable strategy
Not planning for data migration when schemas evolve
Ignoring data quality until downstream consumers report problems
Assuming all data access patterns are known at design time

How to Use This Document

Document schemas as living artifacts that evolve with the system. Include field-level descriptions, valid value ranges, and nullability constraints. Define a data classification scheme (public, internal, confidential, restricted) and label every data store accordingly. Plan for schema evolution from day one.

For AI agents: When modifying data models or queries, reference the data documentation to understand field semantics, access patterns, and privacy constraints. Ensure migrations preserve data integrity and backward compatibility.