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Category: GenAI

  • From Code Review to Responsible Orchestration: A Metaphor for AI Adoption, Preserving Core Values, and the Art of Vibe Coding

    From Code Review to Responsible Orchestration: A Metaphor for AI Adoption, Preserving Core Values, and the Art of Vibe Coding

    Over the last year and a half, I have redefined how I code. Having spent many years building large-scale systems, I knew my process well, but the arrival of AI changed the process itself. What once was structured programming has become what is now called vibe coding, shaping intent, context, and tone through dialogue with AI. By vibe coding I mean guiding AI-generated development through direction and review rather than handing over the work entirely. It is a disciplined way to design and express solutions in language instead of syntax. The shift was not spontaneous. It was a deliberate, methodical exploration of what responsible AI adoption can look like in practice.

    At first, I used AI only for review. That was safe territory: transparent, verifiable, and reversible. I could assess what it produced and identify the boundaries of its usefulness. Early on, it revealed a pattern. Its technical knowledge often lagged behind current practice. It showed its limitations: accurate in parts, but sometimes anchored in older methods. For organizations, the same applies. AI adoption should begin with understanding where the system’s knowledge ends and where your own responsibility begins.

    Gradually, I extended its scope from isolated snippets to more complex functions. Each step was deliberate, guided by process and review. What emerged was less a matter of delegation than of alignment. I realized that my values as a developer, such as maintainability, testing, and clear deployment practices, are not negotiable. They form the ethical infrastructure of my work. AI should never replace these foundations but help protect them. The same holds true for organizations. Core values are not obstacles to progress; they are the conditions that make progress sustainable.

    Metaphors are always risky, and I am aware of that. They can simplify too much. Yet they help clarify what is often hard to explain. My work with AI feels similar to how an organization integrates a new team member. LLMs are not deterministic. They hallucinate, carry biases, and their knowledge is bounded by training data. But then, are humans any different? People join with preconceptions, partial knowledge, and habits shaped by their past. We do not simply unleash them into production. We mentor, guide, monitor, and integrate them. Over time, trust builds through supervised autonomy. The process of bringing AI into a workflow should be no different.

    In both cases, human or machine, responsible adoption is a process of mutual adaptation. The AI learns from context and feedback, and we learn to express intent more precisely and to build systems that preserve oversight. The goal is not perfect control but a continuous dialogue between capability and governance.

    Responsible AI adoption is not about efficiency at any cost. It is about preserving integrity while expanding capacity. Just as I review AI-generated code, organizations must regularly review how AI affects their own reasoning, values, and culture. Responsibility does not mean hesitation. It means understanding the tool well enough to use it creatively and safely. What matters most is staying in the loop, with human judgment as the final integration step.

    So my journey from code review to responsible orchestration mirrors what many organizations face today. The key lessons are consistent:
    • Start small and learn deliberately.
    • Protect what defines you: values, standards, and judgment.
    • Build clear guardrails and governance.
    • Scale only when understanding is mature.
    • Stay actively in the loop.

    AI, like a capable team of colleagues, can strengthen what already works and reveal what needs attention. But it must be guided, not followed. The craft of programming has not disappeared; it has moved upstream, toward design, review, and orchestration. In code, I protect my principles, and organizations should do the same. The future of work lies in mastering this dialogue: preserving what makes us human while learning how to work, decide, and lead with a new kind of intelligence.

  • The Four Facets of Determinism in Large Language Models: Numerical, Computational, Syntactic, and Semantic

    The Four Facets of Determinism in Large Language Models: Numerical, Computational, Syntactic, and Semantic

    Large language models are not deterministic systems. Even when presented with identical input, they may produce slightly different results. This variation arises from both the numerical properties of computation and the probabilistic mechanisms of text generation. Understanding the different forms of determinism that influence this behavior helps explain why models vary and how users can manage that variability. These forms are numerical, computational, syntactic, and semantic.

    Numerical determinism

    At the lowest level, determinism depends on how numbers are represented and processed. Large language models rely on floating-point arithmetic, which cannot represent real numbers exactly. Each operation rounds results to a limited precision. Because of this, addition and multiplication are not associative. For example, when a = 1020, b = -1020, and c = 3, the result of ((a + b) + c) is 3, while (a + (b + c)) is 0 when computed in double precision. These differences occur because rounding errors depend on the order of operations. On GPUs, thousands of operations are executed simultaneously. The order of execution and rounding can differ slightly between runs, which makes exact numerical reproducibility difficult to achieve. This limitation defines the boundaries of numerical determinism.

    Computational determinism

    Computational determinism describes whether an algorithm performs the same sequence of operations in the same order every time it runs. Large language models perform extensive parallel processing, where computations may be split across multiple processors. Even when the algorithm is identical, minor differences in scheduling, optimization, or asynchrony can lead to small numerical differences. Maintaining strict computational determinism would require fixed hardware conditions, execution order, and software versions. In most user-facing systems, these variables are abstracted away, so computational determinism cannot be guaranteed.

    Syntactic determinism

    Syntactic determinism refers to the consistency of the model’s output at the level of exact wording. Language models generate text by sampling one token at a time from a probability distribution. When the temperature or other sampling parameters are greater than zero, randomness enters this process by design. Two identical prompts can therefore produce different word sequences. Setting temperature to zero or restricting the token selection space through top-k or top-p sampling makes the process nearly deterministic, as the model always selects the most probable next token. This ensures stability in the literal sequence of words but often reduces stylistic variation and naturalness.

    Semantic determinism

    Semantic determinism concerns the stability of meaning. Even when the exact wording differs, an LLM can consistently produce outputs that convey the same ideas and reasoning. When a prompt defines a clear goal, specifies format and scope, and provides relevant context, the model’s probability distribution becomes concentrated around a narrow set of interpretations. For example, the instruction “Write a 100-word summary explaining the main human causes of climate change” consistently leads to answers focused on greenhouse gases, fossil fuels, and deforestation, even if the phrasing changes. Semantic determinism therefore captures the reproducibility of ideas rather than words.

    Bringing the four forms together

    These four forms of determinism describe stability at different levels. Numerical determinism concerns how numbers behave. Computational determinism concerns how operations are executed. Syntactic determinism concerns the literal text sequence. Semantic determinism concerns the stability of meaning. Each higher level tolerates more variability than the one below it. In practice, full determinism across all levels is unnecessary. For most uses, maintaining consistent meaning and reasoning is more valuable than reproducing exact numeric or textual forms.

    Determinism and Hallucination

    Hallucination and determinism describe different aspects of a language model’s behavior. Determinism concerns the consistency of responses, while hallucination concerns their factual accuracy. A model can be deterministic yet still generate incorrect information if the most probable response it has learned is wrong. Conversely, a non-deterministic model may produce varied outputs, some of which are correct and others not. Higher determinism ensures that the same statement is repeated reliably but does not guarantee that the statement is true. Clear and well-structured prompts can reduce both variability and factual errors by narrowing the model’s interpretive range, yet determinism alone cannot eliminate hallucination because it governs consistency rather than truthfulness.

    What users can control

    As a user, you have little control over the hardware or execution environment, but you can influence determinism through parameter settings and prompt design.

    • Limited hardware control:
      Users typically cannot influence the model’s underlying hardware, floating-point precision, or internal execution path. These affect numerical and computational determinism but remain outside the user’s reach.
    • Control through generation parameters:
      You can adjust several sampling parameters that directly influence how deterministic or natural the model’s text generation is. Choosing suitable values allows you to balance consistency with creativity.
      • Temperature: Lowering it to around 0.0–0.2 sharpens the probability distribution and makes responses highly repeatable, while higher values such as 0.7–1.0 introduce more variation and a natural writing style.
      • Top-p: Restricts token selection to the smallest set whose cumulative probability exceeds p. Smaller settings such as 0.1–0.3 make the output more deterministic, while values near 0.8–0.9 yield smoother, more natural phrasing.
      • Top-k: Limits selection to the k most likely tokens. Setting k = 1 removes randomness almost entirely, whereas k = 40–50 balances focus with stylistic diversity.
      • Seed: Fixing a random seed, for example 42, ensures that the same internal random sequence is used across runs, producing identical token choices when other settings remain constant. Leaving it unset allows small natural differences between runs.
      • Repetition or frequency penalty: Adjusts how strongly the model avoids repeating words. Lower values around 0.0–0.2 support deterministic phrasing, while moderate values of 0.5–1.0 encourage more varied wording.
      • Presence penalty: Controls the likelihood of introducing new topics. Fixed low values such as 0.0–0.2 promote stable focus, while 0.3–0.8 adds variety and new subject matter.
      • Max tokens and length penalty: Fixing a specific output length and using a length penalty of 1.0–1.2 ensures predictable structure. Allowing flexible length or keeping the penalty close to 1.0 produces a more natural and adaptive flow.
    • Control through prompt design:
      The wording and structure of your prompt strongly affect semantic determinism.
      • Clear, specific, and structured prompts (for example, “List three key points in formal tone”) guide the model toward a narrow range of valid answers.
      • Vague or open-ended prompts widen the distribution of possible meanings and tones.
    • Why you would increase determinism:
      • To achieve reproducible and consistent wording in professional or analytical contexts.To make results easier to verify, compare, and reuse.
      • To ensure predictable tone and structure across multiple generations.
    • Why you might hesitate to increase determinism:
      • High determinism can make responses rigid or formulaic.Reduced randomness may suppress creativity, nuance, and adaptability.
      • It can narrow the exploration of alternative ideas or perspectives.
    • Finding the balance:
      • Favor high determinism (low temperature, fixed seed, defined format) for accuracy, documentation, and controlled output.
      • Allow moderate randomness (slightly higher temperature or top-p) for tasks that benefit from variety, such as creative writing or brainstorming.

    Conclusion

    Determinism in large language models exists in several layers. Numerical and computational determinism describe reproducibility in how calculations occur, while syntactic and semantic determinism describe reproducibility in how ideas are expressed. Users cannot control the hardware environment but can improve consistency through parameter choices and well-designed prompts. Absolute determinism is unattainable in probabilistic systems, but by managing these factors carefully, users can achieve stable and reliable outputs suited to both precise and creative tasks.

  • Privacy-First AI for Document Compliance

    Privacy-First AI for Document Compliance

    Strengthening Document Control

    Organizations are well equipped to review and control their own documents. Yet, there is often a need to further strengthen this process with greater consistency, transparency, and efficiency.

    Laiyertech’s Document Compliance Agent supports this goal by providing a secure, AI-assisted solution for rule-based document validation. Documents are never stored, logged, or cached, which guarantees full privacy. Users have complete control over the rules applied, ensuring that validation is always based on their own standards and requirements.

    Privacy by Design

    The agent operates on hosted LLM solutions provided through the Laiyertech AI Software Platform. This software platform is built on an infrastructure that is 100% owned and operated by a European company and falls entirely under European laws and regulations. The language models used are open-source LLMs, hosted exclusively within this European environment, without any connection to non-EU parties.

    This not only ensures that data remains protected but also allows organizations to innovate with AI while maintaining flexibility in their choice of technology providers. By using open-source LLMs hosted within a European infrastructure, organizations reduce reliance on external platforms and gain greater control over long-term AI adoption.

    AI Governance

    The Document Compliance Agent has been designed with governance and accountability in mind. To ensure transparency and control, the agent separates key roles: the user, who performs the document validation; the rule administrator, who manages and maintains the validation rules; and the prompt administrator, who oversees the interaction with the language model.

    Strategic Independence

    In addition to compliance and privacy, strategic autonomy plays an important role. By developing AI solutions on European infrastructure and open-source models, organizations limit potential dependencies on non-EU providers. This approach helps build trust, resilience, and continuity, even in the face of evolving market conditions or regulatory changes that may influence the availability of AI services.

    Version Management and Auditability

    In addition, version management is embedded in the system, allowing organizations to track changes, maintain auditability, and ensure that every validation can be traced back to the specific rules and prompts applied at that point in time. This structure supports responsible AI use and provides organizations with a clear framework for oversight.

    Practical Example

    An organization’s board resolution often needs to comply with strict internal and external requirements, such as the presence of specific decision elements, references to prior resolutions, or required signatories. With the Document Compliance Agent, these criteria can be captured in a ruleset that automatically checks every new resolution for completeness and consistency. This ensures that documents meet governance standards before they are finalized, reducing the risk of omissions and providing management with greater confidence in the documentation process.

    Guidance and Alignment

    Where needed, Laiyertech can assist in structuring and refining validation rules, so they are practical, effective, and aligned with the chosen LLM. This helps organizations establish validation processes that are accurate, consistent, and transparent.

    Commitment to Responsible AI

    At Laiyertech, we see responsible AI not only as a design principle but as a continuous commitment. Our Document Compliance Agent is one example of how we translate this principle into practice, ensuring data protection, transparency, and accountability remain central as AI adoption evolves.

    Try It Yourself

    The Document Compliance Agent is available for free trial, enabling organizations to evaluate its functionality and privacy features in their own environment.

    Discover how privacy-first AI can support your compliance needs. Begin your free trial today at https://veridoc.laiyertech.ai.

  • 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.