Overview of enterprise GenAI capability layers, from model development to runtime operations.
Generative AI has moved beyond its buzzword status to a usable, practical tool that will support everyday workflows within organizations. The high impact of generative AI has been even more pronounced in low-code platforms. It is not about simply enabling users to automate faster – it fundamentally rethinks the way users create and consider software.
To scale these systems, a breakthrough idea (and action plan) is only the beginning. Scaling requires clarity, trust, and iteration; the best examples of GenAI features (and success) I have observed didn’t have a marketing strategy. They grew based on user trust and real value. Guided experience, transparent sharing, and real feedback loops launched an early prototype to a product that went from zero to 300,000 monthly active users in less than six months. Others moved from zero to 150,000 users and maintained this enthusiasm for even longer.
Just keep that in mind of course, achieving that type of adoption takes a focused approach of delivering valuable Gen AI features. The teams that were successful in this space were ones that went small, learned quickly, and measured success along the way. This disciplined approach is the genesis of the 0-to-1 GenAI playbook.
How to Use a Model for Early Prototyping
Each GenAI project begins with the model. There is a strong temptation to build too much in advance, but the first goal is validation, not over perfection anyway. Usually, in prototype mode, you only need 1) a hosted model, 2) an inference set that is simple and 3) a feedback loop. Then, all the systems and governance, monitoring and compliance can come later.
An overview of enterprise GenAI capacity layers, from model development to runtime.
The image below illustrates the capability layers of enterprise GenAI, from a model to operationalization at runtime. Source: IBM – Generative AI Capability Model…
When I build test iterations, I work to validate a very simple model within a low code canvas. This is often public API based or a model hosted internally. Speed is essential. To get the work in front of the real “user” turns out, to provide insight to how the person interacts, where they are stuck and what s they want to do next.
In one test, people often treated prompts as incomplete or vague. The model had some success, but the users’ behavior gets me to what tasks they prefer to express automation around. This moved me to better prompt design, better onboarding and better data.
Eventually to evaluate success, amongst data generated, engagement, session time and satisfaction. In the beginning, low accuracy or price metric were insignificant. Low curiosity was key. If users choose to return to the experience, you may have something, if users abandon exploring or come back, it’s probably time to start over.
How to Fine-Tune a Model When Initial Accuracy Is Low
Once a prototype is adopted and used regularly, accuracy is the next target. General description models are good for general intent, but tasks performed by an enterprise must have domain accuracy. Fine-tuning the model addresses that problem.
The steps to fine-tune a model must be methodical. I use user feedback (replies and thumbs-down) as examples for training. Turn the reply from a user into a labeled example. Building a well-defined dataset through production logs allows transparency on use and identifies common failure modes that lead to retraining cycles. The updated version of a model must return value to users by letting users and ultimately proving through A/B testing that the user experience improves.
The process of fine-tuning is more than technical work. It is a shared discipline for the teams involved because everyone can agree to measurable outcomes and every improvement in an NLP model can be data-driven, rather than assumptions. Regardless of the impact, small and consistent meaningful improvements build trust, which impacts adoption.
How to Use LLM: Script Framework to Improve Trust and Accuracy
Low-code platforms rely on consistency, and large language models, while powerful, will sometimes not obey. The LLM-to-script framework, brings a structure and predictability to AI-driven workflows.
Instead of executing commands from the user as a direct call, the model first generates a structured script that outlines the intent of the model to act. Then the model is verified, executed, and logged into the user’s workflow system. The system provides a transparent and predictable sequence that causes user level trust to increase.
For example, the user will type “send this report to my manager every Monday,” and the model will not act until it creates an automation script with the necessary triggers and recipients. The system checks based on the user’s reported context details and presents a sequence preview. When the user is comfortable and acknowledges, it executes the existing task. By structuring the process of execution, few or no errors happen and predicts that user workflows are exponentially improved – improving both explainability of the model and user confidence.
The normal user flow in an LLM-driven system depicting how the user prompts from model creation to user verification. Source: Microsoft – How to Evaluate LLMs
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Debugging is easier because engineers observe the errors at the script level instead of trying to understand inside a black-box model. By incorporating a sequence of conversational input followed by structured actions, the LLM-to-script framework allows for continuing conversational style and delivers consistently predictable results.
Evaluating the Accuracy of LLM and User Value
Accuracy is not equivalent to success. What really matters is if users are provided relevant, timely and accurate in practice. Technical accuracy and user experience must evolve together in order for a GenAI product to grow.
To evaluate accuracy, I review from two interrelated perspectives:
Model Accuracy: Concerned with looking at the model’s outputs matching expected outcomes. This includes accuracy in logic, verbiage, or task execution. Model Accuracy captures the technical performance and reliability of the system during an automation test.
User Accuracy: Involves whether the output had met the user’s intent. A response may be technically accurate but contextually irrelevant or unhelpful. Metrics like acceptance and edit ratios, and user satisfaction survey scores observe how the model helps the real user objectives.
When accuracy is established in both dimensions, user value is the subsequent layer to consider. Then I review positive to negative feedback ratio, retention rates, and re-usage to see if the user is engaged, and take a long-term view to their value.
During one launch an feature attained 2:1 positive sentiment through the ongoing improvement to technical accuracy. Users felt supported and confirmed that was the right direction, and as accuracy improved user satisfaction did too.
By establishing model accuracy, user accuracy, and user value, progress is measured meaningfully and contributes to the user experience. Then it is doing what was intended from a performance metric to a user impact.
Applying the 0-1 Framework
When any new generative AI (GenAI) feature is progressing to scalable capability, the right mindset is very important… not one thing becomes the answer. Teams need a mechanism to formally structure a process to engineer creativity, speed, its accuracy, and build trust with the user. Over several launches of different product versions, I have regularly seen one tried and true 0-1 process with only 2-4 simple steps.
Prototype Quickly. By starting out with a working prototype, teams are able to validate user intent before committing to the next level of refinement with speed and precision.
Fine Tune Intentionally. Use real feedback to continuously refine the prototype through iteration and validation within a defined context.
Structure Execution. Create frameworks to include predictability and control into generative systems such as LLM-to-script.
Measure Deeply. The human experience is not solely about efficiency, it is also about user value (importance).
For each stage of the process, precedent systematically builds from the previous stage. And once the cyclone spins out of control, teams will see speed to utilizing increase with every testing of a prototype. The velocity of a user’s feedback cycle is a clear indicator to a team of velocity to learn, velocity to scale, and ultimately velocity to build user trust. The optimum is when engineering and design are paired with data scientists to combine ownership and shared outcomes with success metrics defined. That clarity begins a baseline for working toward deploying a high-performing product.
In Summary: From GenAI Vision to Scalable Reality
The generative AI journey now is not necessarily an adventure of novelty but of execution. Enterprise leaders are not questioning if or whether to adopt or embrace GenAI. Enterprises are now enacting what that will mean. With the focus changing away from novelty to delivery, and innovation in the ambiguous context of implementation; strong success in scaling use of features means a design with a user need-based context, defining variables and effecting change in multiple rounds of feedback so every iteration becomes scaled to build accuracy, precision, and stimulate user confidence.
The 0-1 GenAI framework emphasizes a metric-based mindset; for the process and in working towards continual evaluation and improvement. A manager drives curiosity, an associated feedback loop, the team learns, and iteratively build trust in the concept of maturity and moves every process in learning back to user experience. When ongoing precise execution around precision to execute and evolve a team’s understanding and experience with user need evolves beyond just another layer, GenAI becomes the basis of how every enterprise builds, duties automation, and innovates each layer of a product.
About the Author
Kishor Subedi is a Senior Product Manager with over five years of experience leading Generative AI and automation initiatives in enterprise environments. He has launched multiple 0-to-1 AI features that scaled to hundreds of thousands of users, focusing on building reliable, user-centered AI solutions that simplify workflows and accelerate adoption in low-code platforms.
References
- IBM (2023). Generative AI Capability Model. https://www.ibm.com/architectures/hybrid/genai-capability-model
- McKinsey & Company. (2024). What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
- Microsoft. (2024). A List of Metrics for Evaluating LLM-Generated Content. https://learn.microsoft.com/en-us/ai/playbook/technology-guidance/generative-ai/working-with-llms/evaluation/list-of-eval-metrics
- Microsoft. (2024). A/B Testing Infrastructure Changes at Microsoft ExP. https://www.microsoft.com/en-us/research/articles/a-b-testing-infrastructure-changes-at-microsoft-exp/
- Microsoft. (2023). How to Evaluate LLMs: A Complete Metric Framework. https://www.microsoft.com/en-us/research/articles/how-to-evaluate-llms-a-complete-metric-framework/