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Align capable models with expert judgment.

TazQ provides frontier alignment data: expert-written demonstrations for supervised fine-tuning, step-by-step reasoning traces, and human preference comparisons for RLHF. Domain specialists write, compare and critique model outputs against clear rubrics, so teams can make capable models more useful, honest and reliable.

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Updated RLHF · SFT · Reasoning · Preference data

Definition

What is frontier alignment data?

Frontier alignment data is the human-created data used to shape how advanced AI models behave after pre-training. It includes demonstrations of ideal answers for supervised fine-tuning (SFT), comparisons where experts judge which response is better for reinforcement learning from human feedback (RLHF), and reasoning traces that show how a strong answer is worked out step by step.

Why it matters

Capable isn’t the same as aligned.

The last mile of model quality is decided by people who know what a great answer looks like.

01

Generic raters miss expert errors.

A fluent answer can still be wrong. Spotting subtle mistakes in law, medicine, code or finance takes people who practise in those fields.

02

Preferences need consistent criteria.

Comparisons only teach a model something when every judgment follows the same written rubric, applied the same way across raters.

03

Reasoning has to be shown, not assumed.

Models learn to reason more reliably from worked examples that make each step, assumption and check explicit.

Free: the Frontier Alignment readiness checklist

Seven decisions that make an alignment data project go smoothly, from defining good behaviour to measuring whether the new data changed it.

How TazQ does it

From expert knowledge to aligned behaviour.

Every alignment project follows four steps, shaped around the behaviour you want the model to learn.

  1. 01

    Define what good looks like.

    Agree the target behaviours, domains and failure modes, then turn them into written rubrics and reference examples.

  2. 02

    Match the right experts.

    Select specialists with real experience in each domain and calibrate them on the rubric before production begins.

  3. 03

    Write, compare and critique.

    Experts write demonstrations, rank and explain model responses, and annotate reasoning step by step.

  4. 04

    Review, measure and deliver.

    Senior reviewers audit samples, agreement is tracked across raters, and data ships in your training format.

Deliverables

What you receive.

Formats adapt to your training stack. These are the data types alignment teams ask for most.

Frontier Alignment data types, what each includes, and typical formats
DataWhat it includesTypical formats
SFT demonstrationsExpert-written prompts and ideal responses, covering the tasks, tone and domains you specify.JSONL prompt–response pairs
Preference comparisonsPairs or sets of model responses ranked by experts, with the reasons behind each choice.JSONL with rankings and rationales
Reasoning tracesStep-by-step solutions that show the method, the checks and where an answer could go wrong.Structured JSON or Markdown
Critiques and rewritesExpert feedback on model answers, with corrected versions that fix each issue found.JSONL with critique and revision
Rubrics and guidelinesThe written criteria behind every judgment, versioned and shared with your team.Documentation with examples
Adversarial promptsHard and edge-case prompts that probe safety, honesty and refusal behaviour.Prompt sets with expected behaviour

Want to see one?

Request an anonymised sample of any deliverable. A specialist will share a relevant example where available.

Use cases

Where teams use it.

01Post-training a frontier model

High-quality SFT and preference data that lifts helpfulness and accuracy across many domains at once.

02Domain specialisation

Expert demonstrations and comparisons that teach a model the standards of a specific field, such as law, finance or engineering.

03Reasoning and maths

Worked solutions and graded reasoning that help models plan, check their work and avoid confident mistakes.

04Safety and honesty tuning

Adversarial prompts, refusal judgments and critiques that teach a model when to help, when to decline and how to say so.

Quality & experts

Quality starts with people.

Domain experts, prepared

Specialists are prepared through TazQ Academy and calibrated on each project’s rubric before they write or judge a single example.

Explore TazQ Academy

Rubrics you can read

Every judgment follows written criteria that your team can review, challenge and version as the project evolves.

Measured agreement

Overlapping reviews show where raters agree and where guidelines need sharpening, and senior experts resolve the hard cases.

FAQ

Questions, answered.

01

What is RLHF data?

RLHF (reinforcement learning from human feedback) data is a set of human judgments about model outputs, usually comparisons where a person chooses the better of two or more responses and explains why. A reward model learns from these judgments and is then used to steer the main model towards preferred behaviour.

02

What is the difference between SFT and RLHF data?

SFT data shows the model an ideal answer to imitate. RLHF data shows which of several answers is better, so the model learns preferences and trade-offs rather than a single target. Most post-training uses both: SFT first, then preference data.

03

Why use domain experts instead of general annotators?

In specialised fields, the difference between a good and a bad answer is often invisible to non-experts. Professionals catch subtle errors, judge reasoning quality and write demonstrations that reflect how the work is really done.

04

How do you keep preference judgments consistent?

Each project has a written rubric with reference examples. Raters are calibrated before production, a share of items is reviewed by more than one person, and disagreements feed back into clearer guidelines.

05

Can you write reasoning traces for maths and code?

Yes. Specialists write step-by-step solutions that make the method and checks explicit, and they can grade or correct a model’s own reasoning, step by step.

06

How do we start an alignment project?

Share the behaviours you want to improve, the domains involved and the format you train with. A pilot batch then validates the rubric and quality bar before the project scales. You can outline this in the project brief below.

Related capabilities

All capabilities
05 /↗

Model Integrity

Human evaluation that investigates hallucinations, bias, and failure modes—turning uncertain model behavior into clearer quality signals.

  • Evaluation
  • Safety
  • Robustness
06 /↗

Research Services

Expert-designed benchmarks, calibrated judging, and carefully reviewed synthetic data for teams exploring the next frontier of AI.

  • LLM judges
  • Synthetic data
  • Benchmarks

Start your Frontier Alignment project

Let’s build
what comes next.

A better model starts with a clearer question.
What do you want yours to understand?

For AI teams

Scope your project in about a minute. A specialist reads every brief and replies with next steps.

For human expertsExplore TazQ Academy
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