LLM training data for aligned, tuned and evaluated language models.
AI teams building LLM training dataset pipelines internally often struggle to maintain consistency, QA and reviewer agreement at scale. Hitech BPO’s LLM Training Data Services solve this by delivering domain-aligned, structured datasets for instruction tuning, human preference alignment, red teaming, and retrieval augmentation. We help you meet evaluation benchmarks and safety thresholds while reducing annotation bottlenecks.
We deliver ready-to-ingest training data for large language models, including instruction–response examples, preference-ranked completions, red teaming prompts, multilingual corpora, and benchmarking test sets. Our LLM data services span the full stack of LLM dataset creation, LLM data annotation, LLM data curation, and LLM data preparation—structured to your format and task.
Each dataset is validated with multi-stage QA: reviewer calibration, error audits, labeling guides, format checks, and metadata tagging. When you hire us, you gain structured LLM data sourcing, annotation, and delivery pipelines that reduce rework, improve alignment, and speed up deployment of reliable models.
Data Accuracy
Terabytes of Curated Data
Supported Languages
Reduction in Data Bias
Annotated Samples
Cost Savings
LLM Fine Tuning
We offer fine-tuning services to customize base large language models (LLMs) using task-specific, domain-relevant, and instruction-aligned datasets. Whether you’re targeting customer support, legal research, or technical writing, our LLM fine-tuning data workflows help you build high-performing models aligned with real-world applications. Our services ensure controlled outputs, improved prompt understanding, and optimized generalization.
Preference Data Collection (RLHF – Reinforcement Learning from Human Feedback)
We design RLHF pipelines that combine human judgment with reward modeling to align LLM outputs with ethical, safe and useful behavior. By structuring preference data using pairwise ranking, scoring or comparative evaluations, we enable models to learn nuanced human preferences—critical for instruction-following agents, chatbots, and assistants.
Domain-Specific Corpus Curation & Cleaning
We provide high-quality LLM data curation services by sourcing, filtering and normalizing text corpora from trusted, domain-relevant sources. Our curated datasets enable improved accuracy and contextual understanding across specialized domains. Each dataset undergoes rigorous LLM data preparation steps to ensure clean, balanced and structured inputs for training.
LLM Data Annotation
We annotate large-scale text data for LLMs to support supervised learning, evaluation, and prompt-response generation. Our annotations span multiple NLP tasks—providing structured training data that enhances the quality of large language model training data for high-stakes domains such as healthcare, legal or education.
LLM Benchmarking & Evaluation
We offer custom LLM benchmarking to measure model performance across linguistic tasks, reasoning ability, coherence and safety. Our evaluations help you compare multiple models or assess the effectiveness of fine-tuning across domains and audiences.
Data Formatting for LLM Fine-Tuning Frameworks
We prepare and transform datasets into formats required by common LLM training frameworks. This includes prompt engineering, tokenization and consistent structure across datasets. Our services reduce your pre-processing time and improve integration with LLM training pipelines.
LLM Red Teaming & Model Safety
Our red teaming services simulate adversarial scenarios to test LLM safety and robustness. We expose models to edge cases, malicious prompts and sensitive queries to detect vulnerabilities. This supports safe model deployment and regulatory compliance.
Retrieval-Augmented Generation (RAG)
We support end-to-end RAG pipeline development by curating, indexing and formatting retrieval corpora. This improves factual accuracy, reduces hallucinations and enhances context relevance in generative outputs. Our LLM data sourcing approach ensures high-quality retrieval content across use cases.
We manage sourcing, formatting, annotation, scoring and QA—reducing in-house load while improving consistency.
Every dataset is use-case aligned, from RAG and instruction tuning to red teaming, grounded in real-world LLM data quality standards.
Datasets support version control and reproducibility and plug directly into your LLM data pipeline—no refactoring needed.
We provide technical teams with traceable, audit-friendly Training Data for Large Language Models that shortens dev cycles and lower QA friction.
Want faster, smarter model training?
Get domain-specific datasets, formatted and ready to deploy.
Frequently Asked Questions
What types of data do you provide for LLM training?
How do you ensure the quality of your LLM training data?
Can you create custom datasets for specific LLM applications?
Do you provide data annotation services for LLM training?
What is the role of synthetic data in LLM training?
How can your services help with LLM fine-tuning?
What are the benefits of using your LLM data and services?
Do you support multilingual and low-resource languages?
Do you offer synthetic data generation for LLMs?
What’s the difference between LLM pretraining and fine-tuning data?
What’s next? Message us a brief description of your project.
Our experts will review and get back to you within one business day with free consultation for successful implementation.
Disclaimer:
HitechDigital Solutions LLP and Hitech BPO will never ask for money or commission to offer jobs or projects. In the event you are contacted by any person with job offer in our companies, please reach out to us at info@hitechbpo.com
