Solutions · SFT Instruction Data

Instruction data infrastructure for fine-tuning and alignment

The SFT pipeline produces instruction-response pairs calibrated by experts with written reasoning and peer-reviewed before entering your training set.

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10+
Domains covered - legal, finance, code, science, policy
3-Pass
Write → peer review → expert sign-off on every instruction pair
100%
Human-written originals no LLM recycling in your training data
8 wk
From brief to first benchmark improvement on average
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Instruction TuningChat Fine-TuningDomain-Calibrated PairsExpert Peer ReviewPrompt EngineeringAlignment DataMulti-turn Conversations3-Pass QAInstruction TuningChat Fine-TuningDomain-Calibrated PairsExpert Peer ReviewPrompt EngineeringAlignment DataMulti-turn Conversations3-Pass QA
SFT Instruction Data Pipeline
● INSTRUCTION · WRITING
✓ PEER REVIEW · PASS
⚠ EXPERT FLAG · 1
✓ 5,200 PAIRS · SPRINT 08
Instruction Pipeline

Fine-tuning data that defines what good looks like

Instruction-response pairs go through write → peer review → expert sign-off. Every pair in the set meets the same standard before it reaches your training run.

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Live Annotation Interface

SFT Instruction Authoring Tool

The pipeline generates instruction-response pairs through domain-calibrated authoring with structured flagging, quality scoring, and peer sign-off built into every task.

ConcaveLabel Studio - SFT Instruction · Task #2891 · Domain: Legal / Contract Law
Instruction Prompt
You are a contract law specialist. Explain the difference between a material breach and a minor breach of contract, and describe the remedies available to the non-breaching party in each case. Use clear examples from commercial contracts.
Domain: Legal Type: Explanatory Difficulty: Expert
Expert Response ✓
A material breach fundamentally defeats the purpose of the contract for example, a supplier delivering no goods by the delivery date. The non-breaching party may terminate and sue for expectation damages. A minor breach is partial non-performance where the core obligation is met for example, late delivery of conforming goods. The remedy is limited to compensatory damages without right of termination.
ACCURATE WELL STRUCTURED PEER REVIEWED ✓
Task Type:SFT Pair Domain:Legal / Contract QA Pass:3-Pass ✓ Batch:5,200 pairs · Sprint 08
How It Works

Three things the pipeline does on every project

Domain-calibrated authoring
Instructions written by domain-matched specialists within legal, finance, agriculture, automotive, and technical domains. Not generated and lightly edited, but authored from scratch and peer-reviewed before QA sign-off.
3-pass peer review
Every pair goes through write → peer review → expert sign-off. No pair enters the training set without all three stages passing. Adjudication notes logged and delivered with the dataset.
Zero LLM recycling
All instruction-response pairs are human-original. The pipeline scans for and blocks model-generated content from entering your fine-tuning set as the detection runs at intake, not just at final QA.
Pipeline Capabilities

What the instruction data pipeline delivers

Human-original instruction authoring
All instruction-response pairs authored by domain-matched specialists, not generated by LLMs and lightly edited. The pipeline blocks model-generated content from entering your fine-tuning set at the source, not at QA.
3-pass quality pipeline
Write → peer review → expert sign-off on every pair. No pair enters the training set without all three stages passing. Adjudication decisions and reviewer notes logged and delivered with the dataset.
Task-type coverage across domains
Instruction-following, reasoning chains, summarization, classification, extraction, and multi-turn dialogue across legal, finance, and technical domains. Task coverage mapped to your model's fine-tuning objectives.
What You Get

Instruction data backed by verifiable quality proof

Every SFT project delivers three core outputs alongside the instruction dataset.

Instruction Dataset
Human-original instruction-response pairs in JSONL or your preferred format. Each record includes the task type, domain tag, instruction, response, peer review score, and expert sign-off status which isfully traceable from authoring to delivery.
QA Report with Agreement Scores
Per-task-type quality scores, 3-pass pass/fail rates by domain, LLM recycling scan results, and a log of all adjudicated pairs with reviewer notes. Every metric verifiable against the raw review logs included in the delivery package.
Data Card & Annotation Guide
Full ML data card documenting domain coverage, task type distribution, authoring methodology, LLM contamination scan scope, edge case handling, and quality thresholds applied. Full annotation guidelines included for future auditing.

Ready to build your training data pipeline?

Tell us your domain and use case. We will write 10 sample instruction-response pairs with our expert team and deliver them for your review no cost, no commitment.

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