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Category: GenAI

  • 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.

  • GenAI Sandbox: How to increase AI Quality Management!

    LLMs, RAG, and prompting evolve almost daily. The challenge isn’t building — it’s testing, validating, and improving safely. Enter the GenAI Sandbox: a space for faster iteration, safer deployment, and early validation.

    From our experience with applications where a GenAI component plays a central role, we’ve observed that this part of the system introduces unique challenges and characteristics.

    Architecture

    The GenAI kernel typically includes:

    • The LLM itself or an interface to it
    • LLM tools (e.g., RAG)
    • Prompting and orchestration (including tool selection)

    This kernel generates outputs based on user input, which the broader application then processes. The rest of the application usually handles responsibilities such as process flow, database management, and the user interface.

    Characteristics of the GenAI kernel

    • Limited transparency: For many, the GenAI component functions as a “black box,” with little visibility into how it works or its side effects.
    • High sensitivity to changes: Even minor adjustments can have significant ripple effects. For instance, resolving an unwanted side effect of a prompt in one use case may cause that same prompt to produce unintended results in another. The same applies to model versions, LLM tools, or RAG documents.
    • Rapid evolution: LLMs, their versions, fine-tuning, supporting tools, and especially prompting/orchestration are evolving at a remarkable pace.
    • Continuous improvement potential: As prompting and orchestration techniques mature, the overall performance of the application can steadily improve.

    The need for structured testing

    To enable this evolution responsibly, organizations need a robust test set and procedure, along with an environment to implement, run, and log changes safely. Mature development teams address this by integrating GenAI testing into their DTAP environments, often with sandboxing in place.

    However, this represents only the best-case scenario. In practice, many organizations lack even a basic test environment with evaluators for prompts. This gap isn’t surprising: setting up such infrastructure is both costly and complex.

    The concept of a GenAI sandbox

    This challenge has led to the idea of a dedicated GenAI sandbox environment (potentially cloud-based). Such an environment would:

    • Allow testing without full-scale development
    • Use the same core components as the production GenAI solution
    • Support test sets and evaluators to assess responses effectively

    Because changes to the GenAI kernel are expected to occur far more frequently than changes to the rest of the application, such a sandbox would enable continuous and safe improvements to prompts and orchestration.

    With this approach, any adjustment to the GenAI kernel could be tested quickly. If a DTAP pipeline exists, changes would still progress through it. But even without a complete DTAP setup, this sandbox would already mitigate much of the risk associated with frequent modifications.

    Why not just test with my preferred LLM and chatbot?

    Of course, it is possible to test ideas directly with a preferred LLM or chatbot. However, this approach has significant limitations:

    • If your application relies on RAG with proprietary documents, you may need to replicate that setup in testing, which is not typically supported by standard chatbots.
    • If you want to compare multiple LLMs from different vendors, this is difficult to achieve through a single chatbot interface.
    • Many important parameters and configurations—such as temperature, top-k, context handling, or tool orchestration—are not accessible in consumer-facing chatbots.
    • Testing in isolation does not reflect the end-to-end behavior of the application, where outputs are processed, logged, and evaluated as part of a larger workflow.

    In short, while a chatbot can provide quick insights, it does not provide the controlled, repeatable, and comprehensive environment needed for professional application testing. A GenAI sandbox bridges this gap by replicating the actual architecture and enabling systematic evaluation.

    Additional use case: early-stage validation

    A GenAI sandbox could also add value at the ideation stage of a project. Without building the full solution, teams could experiment with and validate the critical GenAI components of a future application. At this early stage, regulators and stakeholders could already review and assess whether the AI kernel is capable of delivering on the design’s intent

    Laiyertech has developed an AI software platform that is also applied at organizations as a GenAI sandbox. This sandbox can be deployed on our cloud, the organization’s cloud, or in on-premises environments, and is available under a shared source license.

    Our approach is to work collaboratively with your in-house software development team(s) or with your preferred IT vendors to realize an optimal AI application for the organization.