Modern AI is usually perceived as an autonomous technology that can operate continuously and improve without human intervention. However, behind every algorithm, recommendation engine, conversational model, or computer vision system stands the extensive and meticulous work of annotation experts, whose contributions are essential to making these systems function.
This article will examine how the “human in the loop” model works, how scalable QA frameworks and validation pipelines operate, and how automation is combined with human feedback. It will also explore what awaits the data markup and control industry in the coming years.
How modern human-in-the-loop works
The modern human-in-the-loop (HITL) approach in AI is based on a combination of automated algorithms and human intervention. The goal of this approach is to ensure high data quality and model accuracy when fully automatic systems cannot operate independently.
In large projects, HITL involves the work of two main groups of annotators: experts with in-depth knowledge in a specific field, and non-experts who perform more standardized markup tasks under the guidance of senior annotators.
Using automated algorithms, annotators monitor the compliance of annotated materials with project requirements, such as the level of accuracy, type of markup, and classification.
The standard HITL workflow includes:
- Data preparation – collecting, cleaning, and formatting raw data.
- Pre-annotating with models significantly speeds up mass annotation in projects with thousands of repetitive or similar examples, thereby reducing the workload on annotators.
- Markup validation involves annotators checking and correcting previous annotations, handling edge cases, refining class labels, and correcting common model errors.
Most commercial annotation projects operate in a phased, batch-delivery fashion. At each stage, from initial markup to validation, the team hands over a portion of the already annotated material to the customer for quick review to ensure that the implementation meets expectations, the instructions are interpreted correctly, and there are no discrepancies in understanding classes or criteria.
In various projects, the role of domain experts, combined with their experience and knowledge of the context, enables them to correctly interpret data, identify and correct model errors, and ensure the high accuracy of results.
Agrodata
In projects for agrotech companies, data processing from drones or satellites often involves classifying crops, assessing the field’s condition, or identifying stress zones. The model can quickly perform preliminary segmentation, but it is the agronomist experts in the team who determine whether a yellow spot on the field is really chlorosis, fungal damage, or just a peculiarity of lighting.
Domain experts regularly adjust automatic algorithms, and models “learn” much faster from this manual correction than when using general datasets. After several iterations of HITL, the number of errors for individual crops is noticeably reduced.
Medical data
In medical projects, models work well with standard patterns, but lose accuracy in atypical or small formations. During MRI file annotation, medical experts review each suspicious area, refine the boundaries of the lesions, and highlight cases that the model missed or misinterpreted. Regular expert control is the foundation for effective retraining: after a new cycle, the model recognizes complex areas more accurately and operates more stably.
3D data for autopilots
In LiDAR data annotation, automatic models provide only the primary structure. Real urban scenes are multilayered, with overlaps and inaccuracies introduced by sensor data.
In urban environments, objects overlap, distort, and merge into continuous clusters. For example, a cyclist next to a car or a pedestrian in a zone of dense glare – the model is often unable to separate these objects on its own.
A domain expert manually separates objects, determines their orientation, and their trajectory. Without manually refined files, the autopilot will not receive training data that accurately reflects real-world traffic situations.
Validation pipelines and scalable QA frameworks
One of the main tools in this approach is QA sets – specially selected tasks with known “correct” answers, which are used to test annotators and models for accuracy. These control tasks enable the assessment of markup quality in real-time, detect systematic errors, and provide quick corrections for annotators.
In large teams, consistency reviews are used to compare the markup of the same material by different annotators or to check the correspondence between different data types, ensuring consistency between annotators.
To ensure high-quality data markup and model reliability, a multi-level validation approach is used, consisting of four levels:
- Level one – automated checks and pre-markup by models.
At this stage, basic control rules and automated scripts are applied to verify the presence of structural errors, omissions, or inconsistencies in the data. Models can pre-mark the data, creating a basis for further manual verification. This allows you to quickly identify obvious errors and reduce the amount of manual work.
- Level two – manual verification by annotators.
After pre-processing, the data is verified by expert annotators. They assess the correctness of the markup, correct inaccuracies, and add detailed annotations that automated systems cannot always account for. This level provides the first expert assessment of data quality.
- Level three – quality assurance by QA specialists.
At this stage, the QA team conducts a more thorough audit of the markup. QA specialists analyze systematic errors, ensure compliance with standards, and coordinate markup approaches between different annotators.
- Level four – project consolidation and system audit.
At the final level, the results of all checks are collected and analyzed at the project level. Here, systematic errors, inconsistencies between different data types, and general trends in the quality of the markup are identified. Consolidation enables making informed strategic decisions about enhancing processes, clarifying markup rules, and refining model training.
Future outlook
HITL and scalable QA frameworks will remain key components of AI development in the years to come. As data volumes grow, models become more complex, and accuracy requirements increase, there is a need for even more flexible and integrated pipelines. In the context of specific AI model tasks, automated algorithms cannot fully satisfy all training data needs, so the participation of domain experts in the annotation process remains fundamental.
This becomes especially clear with the rapid evolution of large language models (LLMs). Despite their impressive reasoning capabilities and ability to generalize across different domains, LLMs remain highly dependent on human-curated data and constant feedback loops. These models can generate coherent text, summarize documents, classify content, or plan actions. Still, without carefully structured training data, human-validated examples, and continuous correction of edge cases, their accuracy quickly degrades.
The most advanced LLM training pipelines today rely on three HITL layers:
- Human preference feedback (RLHF and RLAIF), where trained annotators or domain experts score responses, identify harmful or incorrect outputs, and define what “good” performance looks like.
- Expert-driven evaluation sets, where specialists in medicine, agriculture, finance, engineering, or legal domains provide gold-standard examples that guide fine-tuning.
- Iterative error correction, where humans review model failures, hallucinations, misinterpretations, and factual inaccuracies, and feed corrected examples back into the training loop.
Without this human-driven quality cycle, even the strongest LLMs drift toward inaccuracies, lose domain precision, or behave unpredictably when facing new data. As these models become integrated into critical sectors, such as diagnostic support, autonomous systems, public governance, and defense analytics, the cost of an unchecked model error becomes exponentially higher.
As Michael Abramov, CEO of Keymakr, emphasizes: “We’re moving away from the old paradigm where an annotator was simply a detail-oriented person who could recognize an object or emotion. In the new reality, professionals are needed: doctors to annotate medical images, programmers to code, architects to create blueprints, marketers for customer insights, and military experts for defense scenarios. The world is changing: more and more people are becoming operators and “trainers” of artificial intelligence. In the future, any one of us might receive an offer to work as an annotator, not just someone clicking buttons, but an expert whose knowledge shapes the intelligence of tomorrow. We already live in this new reality, a world of data labeling and AI training. Those who recognize it and adapt will gain a significant advantage.”
Copyright Photos: Keymakr
