To train better AI, start with human experts

As GenAI matures, so do the expectations placed on it. Clients no longer ask only for generalized data support. They now seek domain-specific expertise to train large language models (LLMs) that can reason with nuance, handle specialist knowledge, and operate safely in high-stakes environments.

At Centific, we’ve recognized that as GenAI projects grow more complex, the bar for quality, speed, and subject matter depth rises just as fast.

We’ve built a scalable solution to meet that demand by combining deep human expertise with an AI-augmented review process. The result: a human-centered, tech-enabled program that delivers high-quality GenAI training data faster, with greater precision and lower cost.

Clients need domain experts, not just data workers

In the early days of GenAI adoption, many projects required people with strong writing or content evaluation skills, especially for prompt development and annotation. But the landscape is shifting. Clients are asking harder questions.

They’re deploying GenAI in sensitive sectors like healthcare, finance, and law. And they want assurance that the models they’re building won’t just echo general knowledge; they’ll handle edge cases, reason ambiguity, and reflect the realities of expert judgment.

As a result, GenAI increasingly must be trained by specialized domain expertise and the ability to think contextually across disciplines. Subject matter experts must not only understand complex topics like quantum physics or infectious disease, they must also know how to interact with LLMs to train them to deliver accurate, high-quality work.

And that creates another layer of challenge: how do we prepare domain experts to contribute meaningfully to GenAI training if they’ve never worked with an LLM? How do we help them experiment, learn, and build confidence, especially when even the trainers are not experts in their fields?

The stakes are high

This is a real-world issue with high stakes. If LLMs misunderstand a concept or miss a subtle edge case, the consequences can be serious. Misdiagnosing pneumonia in a patient with comorbidities, giving poor investment advice, or reproducing biased narratives in educational content are unacceptable errors.

AI isn’t intelligent in the human sense. It doesn’t “know” anything. It processes massive amounts of data, identifies patterns, and predicts plausible next words. Without expert human input, it may sound confident while being entirely wrong. That’s why the partnership between domain experts and AI must be designed with care, oversight, and collaboration.

At Centific, we believe the best way to build trustworthy GenAI systems is by bringing real-world intelligence into the training process. And that starts by supporting experts in applying what they know.

We have created a human-centric, AI-accelerated training pipeline

We’ve created a repeatable model for onboarding domain experts and preparing them for high-impact LLM projects. It begins with a mindset shift: we don’t ask SMEs to memorize or follow rigid instructions.

We invite them into a coaching-driven experience that’s exploratory, immersive, and personal. During onboarding, we run live webinars that demystify GenAI systems. We explain how LLMs “think,” why hallucinations happen, and where subject matter knowledge plays a crucial role. We use analogies, storytelling, and real-world examples—because this isn’t about learning rules; it’s about learning behaviors.

Next, we give SMEs a hands-on taste of typical project types—prompt authoring, annotation, evaluation—through scenario-based simulations. It’s not pass/fail. It’s a “date” with the kind of work they might do. Some click. Some don’t. Like online dating, a personal profile alone doesn’t guarantee chemistry! But by letting SMEs test the waters, we find the right fit for each project.

This is where our innovation kicks in. Centific built an AI-powered review engine capable of assessing SME-authored prompts in seconds—something that would traditionally take human reviewers 45 minutes or more.

The engine was developed through close collaboration between our Learning & Development and Quality Management teams. It draws from our custom scoring rubrics and real-world production insights to evaluate prompts for clarity, logic, relevance, and difficulty.

The results have been striking. In one program, 312 prompts were reviewed by the AI engine and submitted for human QA. The result was a 92% agreement rate. And among those rejected, several could be considered borderline or subject to interpretation. The alignment between AI and human judgment was high. This is evidence that our system is not only fast but dependable.

By integrating this AI-enhanced review process, we’ve reduced total review time by more than 50%, cutting the workload from 45 minutes per prompt to just 20. That saved more than 130 hours on a single project, enabling faster onboarding, quicker iteration, and timely feedback for SMEs.

 And because our platform supports flexible review styles (academic, conversational, evaluative) it helps LLMs learn to respond to the wide range of communication styles they’ll encounter in the real world.

We’re now turning this into a full web-based tool with real-time feedback, human-in-the-loop moderation, and performance dashboards to track quality, impact, and author growth. It’s a model that’s not only scalable. It’s shareable.

This enables substantial business value

By automating the most time-intensive parts of quality assurance while keeping human oversight in the loop, we unlock three key advantages for clients:

• Speed: AI cut prompt review time from 45 minutes to 20 minutes, a 55% reduction.

• Accuracy: The AI review was in line with the human QA 92% of the time.

• Scalability: We can now onboard more SMEs and process more data without burning out teams or compromising quality.

Together, these gains allow us to meet growing client demand for high-quality GenAI training data faster, more efficiently, and with confidence in every output.

Build the future with experts and AI together

We’re entering a new phase in GenAI development that demands more rigor, more creativity, and more human nuance. AI is not here to replace expertise. It’s here to absorb it, learn from it, and apply it at scale. But that only happens if we design systems that empower people to teach it well.

At Centific, we’re proud to help build those systems and to do it in a way that centers people, not just performance. When AI is treated as a tool and not a magic solution, something powerful happens: it becomes a bridge. Between people and technology. Between knowledge and reasoning. Between what machines predict and what humans truly understand.

Explore Centific’s frontier AI data foundry platform.