The MFV Legal Tranche is a rights-cleared, provenance-verified legal reasoning dataset: 759 retrieval cards, 118 canonical packages, and 291 documented corrections derived from two years of California civil litigation that settled favorably against a top-20 defense firm. Licensed for AI training, fine-tuning, and evaluation. A free sample tranche is available on AWS Data Exchange; the full corpus is licensed at $299 for 12 months.
| Metric | Value |
|---|---|
| Retrieval cards | 759 |
| Canonical packages | 118 |
| Documented corrections | 291 |
| Optimized prompts | 3 |
| Validated quality lift | 20% |
| Validation method | Two independent AI judges |
Single author, 100% owned, zero scraped content, no third-party rights. Every document originates from two years of pro se California civil litigation conducted by the author against a top-20 defense firm. The origin of the material is documented in a public court record: Superior Court of California, County of Los Angeles, Case No. 25STCV14949. Post-litigation-era buyers price licensing risk into every training-data purchase — a single-author corpus with a verifiable public-record origin removes that risk entirely: there is no scraped content to trace, no third-party rights to clear, and no ambiguity about who owns the data.
The corpus itself is sanitized for public sale: party names and personally identifying details are redacted throughout, so the dataset ships clean for training use while the public court record independently verifies its origin.
Fine-tuning legal AI models. The tranche captures how legal reasoning actually unfolds in live litigation — claims constructed, opposing arguments answered, errors caught and corrected — rather than the static case-law text most legal corpora contain. Fine-tuning on reasoning traces with documented corrections teaches a model the process of legal argument, not just its vocabulary.
RAG evaluation. The 759 retrieval cards are self-contained, human-authored units of legal knowledge with known relationships to the 118 canonical packages they support. That structure makes them natural ground truth for evaluating whether a retrieval pipeline surfaces the right authority for a given legal question — a labeled retrieval corpus that did not come from synthetic generation.
Legal-reasoning benchmarks. The 291 documented corrections are before-and-after pairs: a flawed legal work product and its repaired version. Correction pairs are benchmark material — they let you test whether a model can spot the same defects a human litigator had to find and fix under adversarial pressure.