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Author: Jurien Vegter

  • Build trust in AI by using it where trust already matters

    Build trust in AI by using it where trust already matters

    Applying AI in ways that strengthen accountability and human judgment.

    Building trust in AI begins by placing it in roles that support existing work rather than replace it. Compliance and quality monitoring are clear examples, as are related areas such as risk management, internal policy adherence, and vendor due diligence. These functions allow AI to provide value without altering core processes.

    Some argue that AI should first be applied where efficiency gains are most visible, automating routine tasks, cutting costs, and streamlining operations. From that perspective, beginning with oversight functions can seem too modest, as automation promises faster returns.

    Yet efficiency that comes at the cost of trust can lead to resistance and weaken confidence over time. A better starting point is in structured areas where established processes guide decisions. Here AI can improve consistency, detect irregularities, and flag potential issues while decisions remain with people, preserving accountability and building a foundation of trust.

    Because established processes stay intact, accountability is preserved, and employees can engage with AI without disruption. This creates the foundation of trust needed for broader adoption.

    Working Within Familiar Structures

    Processes such as compliance and risk management, built on clear standards, documentation, and review, are well suited as entry points for AI. The technology can strengthen consistency, improve monitoring, and surface patterns that might otherwise go unnoticed.

    Because the framework of the work remains intact, employees can engage with AI as a supportive tool rather than a replacement. It also safeguards essential business values such as accountability, reliability, and human oversight.

    Gaining Practical Understanding

    Using AI in areas where results are reviewed and interpreted by people allows organizations to understand where the technology is effective and where limitations remain. This experience helps define the oversight required before AI is applied in more complex or sensitive domains.

    Supporting a Human-Centered Approach

    Using AI in this way reflects a human-centered approach. It gives people the space to learn how to work with the technology and allows organizations to build internal expertise gradually. It ensures that core values remain central as adoption expands.

    By supporting rather than replacing human judgment, AI can become a tool that strengthens trust and enables responsible use across the business.

    Conclusion

    Starting with AI in compliance, risk management, and related oversight functions provides a practical way to build confidence in the technology. It allows organizations to learn from experience and develop a clear understanding of AI’s role and boundaries.

    Laiyertech supports this approach with solutions designed for responsible adoption, emphasizing transparency, data governance, quality, and alignment with established business practices. We welcome your perspective: Where do you see AI offering the greatest potential to improve confidence and trust in your organization?

  • Why Determinism Matters as Much as Hallucinations in LLMs

    Why Determinism Matters as Much as Hallucinations in LLMs

    Building trust in AI systems through deterministic behaviour

    When people talk about the risks of large language models (LLMs), the discussion often focus on hallucinations: cases where a model confidently invents facts that are not true. Much effort is being put into reducing these errors, especially in sensitive domains like medicine, law, or finance. Yet there is another, less visible issue that is just as critical: the lack of determinism in how LLMs generate answers.

    The Problem with Non-Deterministic Behavior

    Determinism means that a system will always give the same answer to the same question. For legal applications, this is essential. Imagine an LLM helping to draft a contract or summarize a court decision. If the same input sometimes leads to one interpretation and sometimes to another, trust in the system will deteriorate. Even when none of the answers are technically wrong, inconsistency can undermine transparency in legal processes.

    The Technical Roots of Non-Determinism

    The roots of this problem lie in how LLMs generate text. With greedy decoding, the model always chooses the most likely next word, producing consistent results but often at the expense of creativity. With sampling, the model allows for variation by occasionally picking less likely words, which can make responses richer but also unpredictable. This randomness, known as non-determinism, may be acceptable in casual uses like creative writing, but in law it can mean the difference between two conflicting interpretations of the same clause.

    Research shows that simply increasing the size of a model or adjusting its inference parameters does not automatically reduce variability to become completely deterministic. In practice, architectural choices, alignment methods, and decoding strategies play a far greater role in making systems dependable.

    Our Solution: Designing for Consistency

    At Laiyertech, in building an application for the juridical market, we have taken this challenge seriously. Our system relies on multiple agents working in both parallel and sequential steps to refine answers and check outcomes. Context is narrowed and prompts are refined, which has made hallucinations virtually disappear. By explicitly accounting for the non-deterministic nature of LLMs, the system ensures that outputs are not only accurate but also as consistent and reproducible as possible. To safeguard this reliability, we use intensive testing regimes, including A/B testing and large-scale validation sets, to continuously monitor and adjust model behaviour. This way, we catch even subtle shifts in performance before they can affect users.

    Taken together, addressing hallucinations alone is not enough. Applications that operate in juridical or other sensitive domains must also design around the model’s non-deterministic nature. Whether through multi-agent architectures, deterministic decoding, or monitoring frameworks, the goal is the same: ensuring that an AI assistant does not just sound right but is also consistent, predictable, and reliable when it matters most.