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Adrosaga

17+ human-led task types for every stage of LLM development.

From prompt collection and preference ranking through red teaming, bias testing, and continuous benchmarking — structured human intelligence data for training, fine-tuning and evaluating large language models.

Real contributors. Targeted by:

  • Language
  • Domain, demographic
  • Expertise
  • Repeatable workflows
  • Evidence-ready outputs
A globe of contributors beside a certificate, ruler, calendar and a hand offering a data card

Six service categories. One workflow for every need.

Whether you need raw training data, human preference signals, structured labels, safety validation, or ongoing benchmarks — each category below maps to a specific stage of your LLM development workflow.

Data panels joined by a node network, evoking captured data

Data Capture

Prompt-response pairs, multi-turn conversations, voice/audio, and scenario data in 100+ languages.

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Annotation & Labelling

Intent, sentiment, named entities, toxicity, and safety labels with annotation confidence scores and quality assurance (QA) flags.

A hand picking a star beside options marked A and B, evoking a preferred choice

Preference & Ranking

Best-of-N (selecting the top response from multiple options) selection, pairwise A/B ranking, and Reinforcement Learning from Human Feedback (RLHF)-style preference data with reviewer rationale and confidence scores.

A magnifier and warning triangle beside a green tick, evoking quality review

Validation & Quality Review

Human review of LLM outputs for accuracy, hallucination, completeness, relevance, and source grounding.

A masked figure struck by lightning beside a shield bearing an eye, evoking adversarial testing

Safety, Bias & Red Teaming

Jailbreak probing, prompt injection, adversarial testing, demographic fairness assessment, and cultural sensitivity review.

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Benchmarking & Continuous Evaluation

Repeatable human-scored evaluations across model versions, markets, languages, and risk categories. Built for ongoing use, not one-off audits.

All 17+ LLM data capture and validation test types.

Coverage is scoped to your model's development stage and use case — not every test type applies to every project. Each test type below maps to a specific workflow need in LLM training, fine-tuning, or post-deployment evaluation.

Contributor targeting is configured per task: country, language, locale, domain expertise, device type, and demographic profile. Low-confidence and high-risk items are automatically routed to senior reviewers or expert panels.

Prompt Response Collection

A stopwatch beside prompt and response messages, evoking timed collection of prompts and answers

Test Description

Captures original human-written prompts and responses for supervised fine-tuning, instruction tuning, or dataset expansion.

Question:

How do I get high-quality, diverse training data at scale across languages and domains?

Example Scenario

  • Build customer support Q&A pairs in 20 countries.
  • Collect local-language explanations for finance, health, education, or ecommerce scenarios.
  • Generate realistic user questions for a banking chatbot before model training.

Multi-Turn Conversation Creation

Several speech bubbles from different speakers, evoking a back-and-forth multi-turn conversation

Test Description

Captures realistic conversations where a user and assistant exchange multiple turns across a scenario.

Question:

How do I train my model on realistic, multi-step conversation flows?

Example Scenario

  • Train an AI tutor to guide students through a maths problem over several turns.
  • Create escalation scenarios for a telco support assistant.
  • Generate real-world travel planning conversations across languages.

Multimodal Data Capture & Validation

A network of connected data points with verified badges, evoking validated multimodal data capture

Test Description

Captures and validates data involving voice, audio, image, video, OCR, captions, or image-to-text reasoning.

Question:

How do I get quality voice, image, or video training data across languages and accents?

Example Scenario

  • Record voice prompts across accents for speech-enabled assistants.
  • Validate image captions for accessibility or ecommerce search.
  • Check OCR extraction from forms, receipts, or ID documents.

Annotation & Labelling

A tag and a bounding-box marker over a document, evoking data annotation and labelling

Test Description

Turns raw text, audio, image, or multimodal content into structured labels for training and evaluation.

Question:

How do I turn raw data into structured training labels at scale?

Example Scenario

  • Label user intent in support tickets.
  • Tag toxic, unsafe, or policy-sensitive content.
  • Identify named entities, sentiment, topic, or emotion in multilingual datasets.

Best-of-N Selection

A shield with a checkmark, evoking selection of the single best, approved response

Test Description

Shows reviewers several responses to the same prompt and asks them to select the best option against defined criteria.

Question:

How do I generate preference data for Reinforcement Learning from Human Feedback (RLHF) at scale?

Example Scenario

  • Choose the best answer from four model outputs for RLHF-style preference data.
  • Compare model responses by tone, accuracy, and safety.
  • Select the best localised response for a specific country or demographic.

Pairwise / A-B Response Ranking

A hand choosing between options A and B, evoking pairwise A/B response ranking

Test Description

Compares two responses and records which is better and why.

Question:

Which model version actually performs better with real people?

Example Scenario

  • Compare GPT-style responses from two model versions.
  • Test whether a fine-tuned model beats the base model.
  • Rank two customer support answers for helpfulness and compliance.

Model Output Validation

Rising arrows in a refresh loop with a checkmark, evoking validation of model outputs

Test Description

Checks whether an LLM answer is accurate, relevant, complete, grounded, safe, and useful.

Question:

Are my model's outputs accurate, complete, and safe enough to ship?

Example Scenario

  • Validate AI-generated answers in a bank loan assistant.
  • Review hallucination risk in legal or policy summaries.
  • Check if an internal HR assistant gives complete and compliant answers.

Hallucination & Factuality Review

A flying pig breaking away from an atom, evoking a check for fabricated or false claims

Test Description

Evaluates whether a model invents facts, misrepresents sources, or gives unsupported claims.

Question:

Is my model inventing information I can't stand behind?

Example Scenario

  • Test an enterprise knowledge-base assistant against source documents.
  • Review product answers for unsupported claims.
  • Check summarisation accuracy for policy, finance, or technical documents.

Multilingual & Localisation Validation

A world map with speech bubbles, evoking multilingual localisation across regions

Test Description

Validates model performance across languages, dialects, locales, idioms, cultural norms, and regional expectations.

Question:

Does my model actually perform in the languages and markets I'm targeting?

Example Scenario

  • Test customer support responses in 25 languages.
  • Validate slang, dialect, and cultural context in Southeast Asia or Europe.
  • Check translated AI answers for meaning, tone, and local appropriateness.

Domain-Specific Expert Review

A globe with a star badge, evoking expert review across specialist domains

Test Description

Uses qualified contributors or expert reviewers to assess outputs in specialist fields.

Question:

Are my model's outputs accurate in the specialist domain I'm deploying into?

Example Scenario

  • Review healthcare triage responses with trained medical professionals.
  • Validate coding answers with developers.
  • Check financial or insurance explanations with domain-aware reviewers.

Dataset Quality & Gold-Standard Validation

Data dashboards flanked by gold stars, evoking gold-standard dataset quality validation

Test Description

Audits training datasets for quality, consistency, duplicates, spam, mislabels, demographic balance, and benchmark reliability.

Question:

Is my training dataset clean, consistent, and reliable enough to fine-tune on?

Example Scenario

  • Clean a multilingual dataset before fine-tuning.
  • Validate gold-standard answers for recurring benchmark tests.
  • Find mislabelled examples in a safety training dataset.

Synthetic Data Review & Correction

A synthetic-data cube orbiting a certified document, evoking review and correction of synthetic data

Test Description

Our global crowd checks the client's AI-generated synthetic data before it is used to train or fine-tune a model.

Question:

Are my synthetic training examples good enough to use — or will they introduce noise?

Example Scenario

  • Review synthetic customer conversations for realism.
  • Check AI-generated safety prompts for bias or policy gaps.
  • Correct synthetic responses before adding them to a training set.

Real-World Scenario & User Simulation Testing

The globe orbited by data nodes, evoking real-world scenario and user simulation

Test Description

Uses contributors to behave like real users in realistic workflows and report whether the model completes the task successfully.

Question:

Does my model actually complete the task from start to finish with real users?

Example Scenario

  • Simulate a business owner applying for a loan through an AI agent.
  • Test an AI travel assistant from search through booking recommendation.
  • Evaluate a HR assistant handling leave, payroll, and policy questions.

Safety, Harm & Policy Testing

A crash-test symbol beside a risk gauge, evoking safety, harm and policy testing

Test Description

Assesses model behaviour against safety policies, harm categories, and client-specific content rules.

Question:

Does my model comply with my safety policy across the full range of inputs?

Example Scenario

  • Test responses to self-harm, hate, violence, sexual, extremist, or dangerous advice prompts.
  • Check whether a chatbot refuses unsafe requests correctly.
  • Validate regulated advice boundaries in medical, legal, or financial contexts.

Red Teaming & Adversarial Testing

A masked figure with a lightning bolt inside a warning ring, evoking adversarial red-team testing

Test Description

Uses targeted human attempts to expose jailbreaks, prompt injection weaknesses, unsafe compliance, or misuse pathways.

Question:

Can someone break my model's guardrails — and how?

Example Scenario

  • Probe an AI assistant for jailbreak vulnerability.
  • Test prompt injection on an internal document-search assistant.
  • Identify ways users can force policy-violating responses.

Bias, Fairness & Representation Testing

A tilted seesaw between two people, evoking a test for bias and fair representation

Test Description

Tests whether responses change unfairly across demographics, regions, languages, cultures, or user personas.

Question:

Is my model behaving consistently across the populations it serves?

Example Scenario

  • Compare hiring assistant responses across gender-coded profiles.
  • Test credit, insurance, or HR wording across demographic personas.
  • Check cultural sensitivity and fairness in multiple markets.

Benchmark & Continuous Evaluation

A balance scale weighing two dashboards, evoking benchmarking and continuous evaluation

Test Description

Creates repeatable human-scored evaluations that track model quality over time across markets, languages, and risk areas.

This requires on-going testing, and comparison data between test cycles. In order for regression testing to be effective, you must replicate the test configuration from previous test cycles.

Question:

How do I track whether my model is getting better — or worse — after each update?

Example Scenario

  • Run quarterly country-by-country LLM performance benchmarks.
  • Compare model versions before and after fine-tuning.
  • Monitor safety, bias, and answer quality after each deployment update.

Tell us what you need. We'll scope the right workflow.

Tell us your model stage, task type, volume, and timeline — we'll scope contributor targeting and workflow to match.

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Adrosaga can also provide targeted human-generated data to train, evaluate and improve AI systems.

Explore AI model training and data capture

Yes. Adrosaga lets organisations commission proprietary human-generated datasets designed around their own AI needs.

Yes. Adrosaga supports multiple independent contributors reviewing the same item.

Yes. Adrosaga is designed to orchestrate projects ranging from targeted specialist testing to large global programs involving thousands of contributors and hundreds of thousands of work items.

Quality comes from task-specific rankings, strong quality controls, and Adrosaga's pledge to pay contributors fairly, promptly and respectfully.

See all FAQs