AI-ready data / Generative AI & LLM Training Data

Generative AI & LLM Training Data

Prepare prompts, responses and judgments for scoped language-model workflows.

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What the service does

From raw input to reviewable structure.

Prepare prompts, responses and judgments for scoped language-model workflows. Labels and instructions are calibrated with your team on a small pilot before larger batches begin.

Typical tasks

  • Prompt creation and classification
  • Response evaluation and rewriting
  • Factuality and helpfulness
  • Instruction following
  • Hallucination review
Typical input

What you provide.

Use-case requirements, approved sources and response rubric.

Typical output

What you receive.

Reviewed prompt-response examples with evaluation labels.

Demo / Illustrative example

Make the output tangible.

This synthetic example shows the structure of a record. It is not a client dataset or a claim of project performance.

Labels with a clear purpose.

Prompt creation and classification is defined in the project guidelines. Ambiguous examples are flagged for review instead of silently forced into a category.

JSONLJSONCSV
{ "prompt": "Summarize this policy", "rubric": "source-grounded", "review": "needs citation" }
Human-in-the-loop quality

Quality is a process. Not a percentage on a page.

Acceptance thresholds, review methods and sampling are agreed for each project. Calibration happens before volume.

LEVEL 1

Annotator review

LEVEL 2

Peer review

LEVEL 3

Quality reviewer

LEVEL 4

Sample audit

LEVEL 5

Client feedback loop

We document uncertainty and disagreement, revise guidelines with your team and keep an audit trail of corrections. Specialist medical, legal or financial review is available subject to project requirements and qualified reviewer availability.

Use cases

A fit for your data workflow.

LLM buildersBusiness assistants
Supported formats

Agree the schema first.

JSONLJSONCSV

Shared responsibilities.
Clear delivery options.

You retain responsibility for source rights, lawful access, required approvals and intended use. We agree secure transfer, retention and deletion arrangements in the project scope.

  • Client-approved guidelines and representative inputs
  • A project owner for edge-case decisions
  • An agreed acceptance rubric and sample audit
  • Pilot, batch or milestone-based delivery
  • Versioned exports and a documented handover
Project questions

Scope the work with confidence.

What do we need to provide?

Use-case requirements, approved sources and response rubric. You also provide lawful access and usage rights, security requirements, acceptance criteria and a project owner who can resolve ambiguities.

How is annotation quality measured?

We agree a task-specific rubric, calibration pilot and sampling plan. Peer review, quality review and client feedback inform acceptance. No universal accuracy percentage is advertised.

Can you handle specialist subject matter?

Specialist medical, legal, financial or expert RLHF work is available subject to project requirements and qualified reviewer availability. Suitability is confirmed during scoping.

What delivery options are available?

Pilot batches, milestone-based deliveries or a scoped recurring workflow. Typical formats include JSONL, JSON, CSV; exact schemas, tools, volumes and timelines are agreed before starting.

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