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LLM Training Data Services

LLM Training Data Services help enterprise AI teams create, refine, and validate the datasets used to train, adapt, and improve large language models (LLMs). Argos Data supports training data programs across instruction tuning, supervised fine-tuning, preference data, multilingual datasets, domain-specific examples, prompt-response pairs, reasoning demonstrations, and model-ready human feedback.

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Overview

Designed around your model objective.

Each program is designed around the model objective, target tasks, domain requirements, language needs, and reviewer expertise. Most programs are delivered through Argos Myriad — the Argos Data Platform — with its customizable tooling providing the task environment, embedded QA controls, and secure expert workflows. For clients who prefer to operate inside their own platforms or through structured file exchange, programs are configured to integrate accordingly.

Use cases

Where LLM Training Data Services is applied.

Creating instruction-response datasets for supervised fine-tuning (SFT)

Developing prompt-response pairs, demonstrations, rewrites, and model-preferred examples

Building domain-specific training data for enterprise workflows and specialized use cases

Producing multilingual and locale-specific training data for global AI systems

Generating preference datasets that feed RLHF, DPO, and human preference modeling workflows

Validating training data for quality, consistency, safety, relevance, and downstream usability

Related services

Three ways we train.

Each program is built around the model objective, target users, operating conditions, and performance requirements.

LLM Pre-Training

Domain-specific pre-training data services for building stronger, more relevant LLM foundations

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Reinforcement Learning from Human Feedback (RLHF)

End-to-end RLHF training datasets built from expert human feedback for model alignment and optimization

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Retrieval-Augmented Generation (RAG)

Human-in-the-loop data preparation for retrieval-augmented LLM workflows

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Why Argos

Training data, treated as an engineered AI data operation.

The risk

LLM performance depends on the quality, relevance, and consistency of the data used to shape model behavior. Generic or poorly governed training data introduces noisy signals, weak domain adaptation, hallucination risk, and unreliable outputs in production.

Our approach

Argos Data combines multilingual depth, domain specialists, and structured operational governance to deliver training data that holds up under enterprise review. We define task criteria, data formats, reviewer qualifications, and validation rules before production begins. Datasets are consistent, auditable, and ready for downstream model development.

Why it matters

For enterprise AI teams, this makes training data a measurable input into model performance, connecting reviewer expertise and quality controls directly to instruction following, domain accuracy, and multilingual reliability in production.

Outcome

High-quality, model-ready datasets for training, adaptation, alignment, and continuous improvement.

LLM Training Data Services give enterprise AI teams high-quality, model-ready datasets for training, adaptation, alignment, and continuous improvement. The result is stronger instruction following, better domain performance, improved multilingual reliability, reduced model error, and more dependable LLM behavior in production environments.