Large language models learn their behavior from human judgment. Which answer is more helpful? Is this response accurate, safe, and well reasoned? Does this code actually work? The quality of those judgments sets the ceiling on how good the model can become — and inconsistent or careless ratings quietly make models worse.
Our RLHF services provide the human feedback data AI teams need to train, fine-tune, and evaluate LLMs: preference rankings, supervised fine-tuning examples, response ratings, red-team prompts, and evaluation sets — produced by trained reviewers with clear guidelines and multi-step quality review. Software engineering and code tasks are our core strength; other subject areas are scoped case by case.
What are RLHF services? RLHF (reinforcement learning from human feedback) services provide human-generated data used to align and improve large language models. This includes preference data, where reviewers compare and rank model responses; supervised fine-tuning (SFT) data, where experts write ideal prompt-response pairs; rubric-based ratings of helpfulness, accuracy, and safety; red-teaming prompts that probe for failures; and evaluation datasets used to measure model quality.
RLHF Services at a Glance
Question | Answer |
|---|---|
What it is | Human feedback data for training, fine-tuning, aligning, and evaluating LLMs |
Data types | Preference rankings, pairwise comparisons, SFT prompt-response pairs, rubric ratings, red-team prompts, evaluation sets |
Core strength | Code generation, code review, debugging, and software engineering tasks |
Formats | JSONL and custom schemas compatible with common fine-tuning and preference-optimization pipelines (RLHF, DPO) |
Quality process | Written guidelines, calibration rounds, gold-standard items, multi-step review, agreement checks |
How to start | A pilot batch on your own prompts, reviewed by you before scaling |
When You Need RLHF Data
Your model's answers are fluent but unhelpful. It needs human preference signals to learn what "good" looks like.
You're fine-tuning for a domain or task. You need high-quality examples written by people who understand the work.
You need to measure progress. You want a trusted evaluation set before each release.
Safety and reliability matter. You need red-team prompts and ratings to find failure modes before users do.
Your current labels are inconsistent. Crowd-sourced ratings disagree too often to be useful.
Coding quality is the goal. You need reviewers who can actually run, read, and judge code.
Our RLHF and LLM Data Services
Preference ranking and comparison
Reviewers compare two or more model responses to the same prompt and rank them against your criteria, producing preference data for RLHF and direct preference optimization (DPO).
Supervised fine-tuning (SFT) data
We write high-quality prompts and ideal responses that show the model exactly how to behave for your use cases.
Response rating
Reviewers score responses against a rubric — for example helpfulness, correctness, completeness, tone, and safety — with short written justifications.
Code generation and review data
Experienced developers write coding tasks and reference solutions, review model-generated code for correctness and quality, identify bugs, and rate explanations. See our dedicated AI training data service for AI labs.
Red teaming
We write adversarial and edge-case prompts that probe for unsafe, incorrect, or policy-breaking behavior, and document the failures found.
Evaluation datasets
We build held-out test sets with reference answers and scoring rubrics, so you can measure model quality consistently across versions.
RAG and agent evaluation
We create question sets with expected sources and answers to evaluate retrieval-augmented generation systems and AI agents, connected to our RAG data pipeline work.
Guideline design
We help write and refine annotation guidelines and rubrics, because clear guidelines are the single biggest driver of consistent feedback data.
RLHF Data Types Explained
Data type | What reviewers do | Used for |
|---|---|---|
Preference pairs | Choose the better of two responses | Reward models, DPO |
Rankings | Order several responses from best to worst | Reward modeling |
SFT examples | Write the ideal response to a prompt | Supervised fine-tuning |
Rubric ratings | Score responses on defined criteria | Evaluation and reward signals |
Red-team prompts | Try to make the model fail | Safety testing |
Evaluation sets | Write questions with reference answers | Release testing and benchmarks |
How We Keep Feedback Consistent
Clear guidelines and rubrics, with worked examples of good, bad, and borderline responses.
Calibration rounds where reviewers rate the same items and differences are discussed and resolved.
Gold-standard items mixed into batches to check accuracy continuously.
Multi-step review, with senior reviewers checking difficult or disputed items.
Agreement checks on overlapping items, reported to you with each batch.
Written justifications for ratings, so you can audit why a choice was made.
How It Works
Step 1: Scope the task
We agree the data type, domain, volume, rubric, and timeline.
Step 2: Guidelines and calibration
We draft guidelines with you and run calibration rounds with reviewers.
Step 3: Pilot batch
We deliver a pilot batch on your prompts for you to review.
Step 4: Scale
Once quality is approved, we scale the reviewer team and deliver in batches.
Step 5: Quality reporting
Each batch comes with agreement scores, gold-item accuracy, and notes on edge cases.
Step 6: Iterate
We refine guidelines and focus on hard cases as your model improves.
Data Security
NDA before any prompts or model outputs are shared
Work inside your platform or tooling when required
Access limited to the assigned reviewers
No reuse of your prompts, outputs, or data
Engagement Models
Model | How it works | Best when |
|---|---|---|
Pilot batch | A small batch to test guidelines, quality, and fit | You're evaluating a feedback data partner |
Project-based | A defined volume of data at an agreed price per task or per hour | You have a clear dataset to produce |
Dedicated reviewer team | A trained team working continuously on your tasks | You need ongoing feedback data for model iterations |
Who This Is For
AI labs | Companies fine-tuning LLMs | AI product teams | Coding assistant builders |
|---|---|---|---|
Need consistent preference and evaluation data at scale | Need SFT and preference data for a specific domain or task | Need evaluation sets and red teaming before launch | Need developer-quality code review and generation data |
Why Work With Codersarts
We're an AI and software engineering company, so our reviewers understand how models are trained and how code really works. We focus on consistency — through guidelines, calibration, and gold items — and we're upfront about which domains we cover well. You start with a pilot and scale only when the quality meets your bar. Our wider data labeling services cover text, image, audio, and document data.
Frequently Asked Questions
What is RLHF?
RLHF, or reinforcement learning from human feedback, is a method for improving language models using human judgments — typically people comparing and ranking model responses — to teach the model which outputs are preferred.
What's the difference between RLHF and SFT data?
SFT data consists of ideal prompt-response examples written by people and used for supervised fine-tuning. RLHF data captures human preferences between responses, used to train reward models or optimize the model directly.
What is DPO data?
Direct preference optimization (DPO) uses pairs of preferred and rejected responses to align a model without a separate reward model. Our preference pair data works for both RLHF and DPO.
Which domains do your reviewers cover?
Software engineering and code are our core strength. General writing, reasoning, and many business topics are also covered. Specialist subject areas are scoped case by case, and we'll tell you honestly if a domain isn't a good fit.
How do you ensure consistent ratings?
Through detailed rubrics, calibration rounds, gold-standard items, multi-step review, and agreement checks reported with every batch.
How much do RLHF services cost?
Pricing depends on task complexity, required expertise, and volume. Code and expert tasks cost more than general ratings. We quote per task or per hour after the pilot scope is agreed.
How fast can you start?
A pilot can usually begin soon after guidelines are agreed. Scaling to larger volumes depends on the domain and reviewer expertise required.
Can you work in our annotation platform?
Yes. We can work in your internal tooling or a third-party platform, or set one up if needed.
Related Data Services
AI Data Services — full data preparation for AI projects
LLM Development — fine-tuning and LLM application development
Unstructured Data Processing — turn documents into model-ready data
Data Quality — quality checks for training and evaluation data
Ready for Better Human Feedback Data?
Tell us about your model, the behavior you want to improve, and the data type you need. We'll propose guidelines and a pilot batch so you can judge the quality yourself.
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