RLHF Data and Human Feedback from Domain Experts
Align your LLM with the judgment of the people who know the domain. Shaip delivers RLHF preference data, rewrites and rationales from vetted subject-matter experts, calibrated, quality-audited and formatted for your training pipeline.
What reinforcement learning from human feedback is
Reinforcement learning from human feedback (RLHF) is a training method that aligns a language model with human preferences. Instead of learning only from example text, the model learns from people comparing its outputs: humans rank or choose between candidate responses, a reward model is trained to predict those preferences, and the language model is then optimized to produce responses the reward model scores highly.
The quality of the result depends almost entirely on the quality of the human preference data. Shaip provides that data: preference rankings, pairwise comparisons, corrected responses and written rationales, produced by domain experts and native speakers, delivered in the formats used by PPO, DPO and related alignment methods.
What a preference dataset contains
Every item Shaip delivers carries these parts, so your reward model or DPO run sees the same structure from the first pair to the last.
| Component | What it is | Who produces it at Shaip |
|---|---|---|
|
Prompt
|
The user request, drawn from your logs, your taxonomy or written by Shaip to cover the scenarios you care about | Prompt writers, domain experts |
|
Candidate responses
|
Two or more model outputs to the same prompt, from one model or several | Your model(s); Shaip can generate from your endpoints |
|
Preference label
|
Which response is better, as a pairwise choice, a full ranking or a graded margin | Domain experts, calibrated to your rubric |
|
Rationale
|
A written reason for the preference, tagged to rubric dimensions (accuracy, safety, helpfulness, tone) | Domain experts |
|
Corrected response
|
An expert-written ideal answer where none of the candidates is acceptable | Domain experts |
|
Metadata
|
Domain, language, difficulty, scenario type, evaluator agreement | Shaip QA |
Your trusted partner in delivering human-aligned RLHF solutions
At Shaip, we provide comprehensive RLHF solutions designed to align AI models with human expectations. Our offerings include:
Human-guided feedback loops
Enhance model performance by integrating real-time feedback from skilled annotators.
Customizable annotation formats
Adapt labeling workflows to meet the unique requirements of your project.
Curated domain-specific datasets
Develop high-quality datasets to optimize AI fine-tuning while ensuring unbiased results that comply with industry standards and regulations.
Error detection & hallucination recognition
Identify and rectify model inaccuracies, minimizing misinformation, hallucinations, and biased responses to ensure high-precision outputs aligned with ethical AI principles.
Prompt optimization & rewriting
Improve AI-generated responses by refining prompts for enhanced coherence, contextual accuracy, and relevance tailored to specific industry use cases.
Multi-language prompt generation
Enable AI applications to support global audiences with language-specific prompt structuring and translation in 100+ languages, ensuring fluent and culturally accurate responses.
What a Shaip RLHF project delivers
Shaip runs the project end to end with its own experts. You receive a dataset, its quality report and a summary of what it tells you about the model.
Preference data
Pairwise comparisons, k-way rankings or graded margins, at volumes from a 2,000-item pilot to 500,000+ items using Ubiquity's global delivery capacity.
Formats
JSONL in the chosen/rejected structure used by DPO and reward-model training; ranking arrays for PPO pipelines; CSV for review; direct delivery into your platform via API.
Rationales and taxonomy
Every preference carries a reason tagged to your rubric; a roll-up shows which failure modes drive rejections.
Corrected responses
Expert-written ideal answers for SFT where candidates fail, in the same JSONL.
Quality reporting
Calibration results, blind double-scoring on a sample, weekly inter-rater agreement (target 98%), drift alerts.
Coverage
20+ domains and 50+ languages, with evaluators matched on both; healthcare, legal, finance and code by credentialed professionals.
RLHF vs evaluation: which one you need
RLHF changes the model; evaluation measures it. Most alignment programs need both in sequence: evaluate to find the failure modes, collect RLHF data that targets them, then evaluate again to confirm the gain and catch regressions. The same Shaip experts and rubrics serve both, so findings from an evaluation feed directly into the next preference dataset.
RLHF data
- Purpose
- Change model behavior
- Output
- Preference labels, rationales, corrected responses
- Used in
- Reward-model and DPO/PPO training
Expert model evaluation
- Purpose
- Measure model behavior
- Output
- Scores, rankings, error taxonomies, findings
- Used in
- Release gates, vendor comparison, regression tracking, judge calibration
RLHF, DPO and the data they need
Direct preference optimization (DPO) and similar methods skip the separate reward model and optimize the language model directly on preference pairs. The training math differs; the data does not. Both need well-calibrated human preferences with clear rationales, and DPO is if anything more sensitive to label noise because there is no reward model to smooth it.
Shaip delivers the same chosen/rejected dataset for either route and can add graded margins where your method uses them.
Enhance Model Performance with RLHF
Reinforcement Learning with Human Feedback (RLHF) helps large language models (LLMs) align better with human preferences. By using expert-curated datasets, your models can deliver accurate, context-aware results while handling complex tasks with ease.
- Improve contextual understanding and decision-making.
- Minimize biases by iteratively refining model behaviour.
- Align AI outputs with ethical standards and real-world expectations.
How a project runs
Typical time from brief to first delivery: 10 days.
Brief
Target behaviors, failure modes, domains, languages and the training method (PPO, DPO, other) so the format is right from day one.
Rubric and calibration
Shaip drafts the preference rubric with anchor examples; experts calibrate on 100–200 items and only those above the agreement threshold enter production.
Prompt sourcing
From your logs, your taxonomy, or written by Shaip to cover the scenario distribution you need.
Preference collection with QA
Blind double-labeling on a sample, seeded gold pairs, weekly agreement reports.
Delivery
JSONL or CSV, plus a findings summary of the failure modes that drove rejections.
Domain-Specific Knowledge for Unmatched AI Accuracy
Shaip stands out for its expertise in delivering domain-specific data solutions across a variety of industries, including healthcare, finance, e-commerce, and more. With a global team of subject matter experts, we ensure top-notch data quality tailored to your unique business needs.
Why choose Shaip for RLHF?
Optimize your LLM with Shaip’s RLHF solutions by leveraging generative AI expertise, human feedback, and unmatched data security
High-quality human feedback
Our global team of experts delivers precise, domain-specific insights to refine AI models.
Optimized model alignment
Leverage human-in-the-loop processes to enhance model accuracy, relevance, and responsiveness.
Bias reduction
Minimize bias by incorporating diverse, high-quality feedback data to create fair and balanced AI models.
Generative AI expertise
We specialize in fine-tuning generative AI models through RLHF, ensuring better alignment with human expectations.
Data security & compliance
With SOC 2 Type 2 certification, we uphold the highest standards of ethical data handling and privacy.
Featured Clients
Empowering teams to build world-leading AI products.
Take your AI models to the next level with Shaip's RLHF solutions
From data collection to annotation, licensing, and validation, we'll help you get to market faster, with data you can trust. Not sure whether you need RLHF data or an evaluation first? Talk to us and we'll scope both.
Contact UsFrequently Asked Questions (FAQ)
1. What is reinforcement learning from human feedback (RLHF)?
RLHF is a method for aligning a language model with human preferences. Humans compare model outputs, a reward model learns to predict those preferences, and the language model is optimized to produce responses the reward model rates highly. Its effectiveness depends on the quality and consistency of the human preference data.
2. What is the difference between RLHF and DPO?
RLHF trains a separate reward model and then optimizes the language model against it with reinforcement learning. DPO optimizes the language model directly on preference pairs, with no reward model. Both need the same input: human preference data with chosen and rejected responses.
3. How much preference data does RLHF need?
Less than pre-training or SFT, but quality matters more. Targeted programs often start with 5,000 to 20,000 well-calibrated pairs per behavior area; broad alignment programs run into the hundreds of thousands. Shaip scopes the volume to the behaviors you want to change.
4. Who provides the human feedback?
Shaip’s own vetted subject-matter experts and native speakers: physicians, lawyers, financial analysts, software engineers and linguists across 20+ domains and 50+ languages. Shaip runs the project end to end; it does not supply annotators into a client’s workflow.
5. How is the quality of RLHF data measured?
Calibration before production, blind double-labeling on a sample, seeded gold pairs and weekly inter-rater agreement reporting, with a 98% agreement target. Every preference carries a rationale so disagreements can be audited.
6. How do RLHF and model evaluation fit together?
Evaluation finds the failure modes; RLHF data targets them; evaluation confirms the improvement. Shaip uses the same experts and rubrics for both, so evaluation findings feed directly into the next preference dataset. See Expert Model Evaluation.
