Previously: In the first three posts, I introduced why Responsible AI matters1, described the 8 Tenets of Responsible AI2, and walked through a simulated restaurant scenario showing what happens when those tenets are skipped3. In this post, the discussion shifts from theory and failure scenarios into implementation.
The company and individual names used in this product example are fictitious and are intended for educational purposes only.
It does not constitute legal advice and should not be relied upon as such. For any legal concerns related to AI compliance, data privacy, or regulatory obligations, please consult a qualified legal professional.
The previous article ended with the Lattice Culinary Systems company producing an action plan to improve their AI-powered chatbot to retain Maison Verdan as a client. The product and development teams at Lattice Culinary Systems reconvened to start with the tenets of Responsible AI.
This article walks through what it actually looks like to design an AI-powered feature end-to-end. The goal is not just to describe the components, but to show how the tenets of Responsible AI influence real product decisions.
From Ideation to Production
Before we dive into the details, it helps to outline the end-to-end journey from ideation to production for this AI-powered feature. In practice, product definition, development, and Responsible AI considerations evolve together. In my experience, it can be a challenge to separate product definition from AI behavior, but, in reality they evolve together. It is iterative, and often messy, especially when working with AI systems where behavior can change based on inputs, data, and model updates.
The journey steps are assigned either to the Products team and/or the Development team.
Products
| Journey Step | Description |
|---|---|
| Use Case Design | Identify user workflows, intents, and where AI adds value |
| Problem Definition | Clearly define the user problem, business value, and success metrics |
| Risk Identification | Map risks across Responsible AI tenets2, 13 |
Use Cases are constantly being validated and can change depending on the perception of the target audience.
Development
| Journey Step | Description |
|---|---|
| System Design | Define LLMs, tools, knowledge bases, and guardrails |
| Workflow Design | Design orchestration paths and control points |
| Implementation | Build orchestration and integrate components |
| Evaluation & QA | Test AI-specific risks such as bias, hallucination, and prompt injection |
| Deployment | Release with monitoring, controls, and rollback capability |
Products and Development
| Journey Step | Description |
|---|---|
| Monitoring & Feedback | Track usage, failures, and user feedback |
| Iteration | Continuously refine prompts, workflows, and models |
Monitoring the behavior of the AI is crucial to ensure that you can demonstrate controllability as well as explain how the AI derived responses. Further, by monitoring user feedback (i.e., both positive and negative), product definition can be changed quickly to ensure the AI is both safe to use and provides the best user experience possible.
To be clear, this article focuses on the product definition side of the AI-powered feature; however, the components map directly into the tenets of Responsible AI. That is why the development perspective is part of this discussion.
Defining the Use Cases
Everything starts with the use cases, but not in the way most teams expect. But let’s be honest: in a world where AI capabilities are evolving rapidly, this step is rarely complete upfront. Teams often need to make informed assumptions, release early, and validate those assumptions through real usage and feedback.
One other thing, I am a big fan of Agile, but only where it makes sense. The format of the user stories4 below will look familiar.
In this example, the primary users are restaurant managers and kitchen staff working inside a food inventory system. Their goals are to ensure menu availability, manage ingredients efficiently, and respond quickly during peak operation times. Building on the scenario from the previous article, the chatbot supports the following real-world use cases:
| Use Case | User Story |
|---|---|
| How-To Guidance | As a manager, I need to ask how-to questions so that I can use the system effectively without formal training. |
| Order Creation and Updates | As a manager, I need to create and update orders so that ingredients arrive on time and the kitchen can operate without disruption. |
| Inventory Interrogation | As a manager, I need to check inventory in real time so that I can make informed decisions during service. |
| Supplier Integration | As a manager, I need to interact with supplier systems through MCP (or similar) so that I can check stock levels and place orders. |
| Dish Recommendation | As a chef, I need recommendations based on available ingredients so that I can maximize menu availability. |
| Menu Adjustment | As a manager, I need to update menu options when ingredients are not available in time for opening. |
| Manual Inventory Adjustment | As an inventory manager, I need to update ingredient quantities to reflect real-world changes. |
| Ingredient Override | As a chef, I need to override ingredient quantities to accommodate specific client needs. |
| Feedback Collection | As a user, I need to provide feedback on responses generated by the AI so that the product team can improve the system. |
These are not theoretical use cases. They reflect the types of interactions that happen during real service hours, when speed and accuracy matter.
Mapping Use Cases to Responsible AI Tenets
Each use case introduces different types of risk, and if you don’t map them explicitly, they tend to show up later in production. Mapping them to Responsible AI tenets ensures that those risks are explicitly considered in the design.
| Use Case | Risks | Responsible AI Tenets |
|---|---|---|
| How-To Guidance | Incorrect or misleading guidance, hallucinated instructions | Veracity, Explainability |
| Order Creation | Incorrect orders, unauthorized changes, operational disruption | Controllability, Governance, Safety |
| Inventory | Exposure of sensitive data, inaccurate inventory insights | Privacy & Security, Veracity |
| Supplier Integration | Improper external calls, data leakage, unintended transactions | Safety, Governance, Privacy |
| Recommendations | Biased or irrelevant recommendations, poor decision support | Fairness, Veracity |
| Menu Adjustment | Incorrect menu changes, lack of traceability for decisions | Controllability, Explainability |
| Overrides | Unauthorized overrides, lack of auditability | Controllability, Governance |
| Feedback | Loss of feedback signals, inability to improve system behavior | Transparency, Explainability |
AI-Powered Feature Requirements
Before jumping into architecture, the team should define the capabilities the system actually needs to support these use cases. The table below extends the previous table as it includes AI-specific technical components mapped to both the Use Case and the tenets. The last column describes what should be considered for each Responsible AI tenet.
| Use Case | Responsible AI Tenet | Components | Design Considerations |
|---|---|---|---|
| How-To Guidance | Veracity, Explainability | LLM, Knowledge Base (RAG), Guardrails | Ground responses with RAG, cite sources, and indicate AI usage in the UI |
| Order Creation and Updates | Controllability, Governance, Safety | Internal Tools, Orchestrator, LLM | Validate inputs, enforce permissions, log actions, and support approvals for high-risk changes |
| Inventory Interrogation | Privacy & Security, Veracity | Internal Tools, Orchestrator, LLM | Ensure data access controls and return grounded, up-to-date inventory data |
| Supplier Integration | Safety, Governance, Privacy & Security | External Connectors (MCP), Orchestrator, Guardrails | Validate external calls, constrain parameters, and audit integrations for MCP |
| Dish Recommendation | Fairness, Veracity, Explainability | LLM, Knowledge Base, Guardrails | Test for bias, ground recommendations in inventory, and capture prompt/context traces |
| Menu Adjustment | Controllability, Explainability | Internal Tools, Orchestrator, Human Approval | Introduce approval gates, provide rationale, and maintain traceability of changes |
| Manual Inventory Adjustment | Controllability, Governance | Internal Tools, Orchestrator | Require authenticated updates, log changes, and enable rollback where needed |
| Ingredient Override | Controllability, Governance | Internal Tools, Orchestrator, Human Approval | Allow overrides with justification, enforce role-based access, and log decisions |
| Feedback Collection | Transparency, Explainability | Feedback Capture, Logging, Observability | Capture thumbs up/down, tie feedback to traces, and use signals for continuous improvement |
Although the UI is not mentioned as a Use Case, it is the interface that will be optimized to provide the best user experience. When designing the UI, the user needs to know when they are interacting with AI and how to interact with it to satisfy the tenet of Transparency.
Chatbot AI Components
Now let's discuss the components of the AI-powered chatbot. We will map the tenets of Responsible AI to the components rather than dive into the technical implementation of each component (e.g. how an observability tool was chosen, or the best embedding model to use for the Knowledge Base, etc.).
There are several AI design patterns the Lattice Culinary Systems design team could use5, 6, 7, 8, 9, 10, 11, 12. They decided to employ an orchestration pattern for the chatbot. Orchestration is what turns a collection of AI components into a controlled system. Instead of relying on a single model to do everything, an orchestration layer coordinates workflows, tool usage, guardrails, and approval points. Without this layer, the system quickly becomes unpredictable, especially when it starts interacting with real data and real users. That distinction matters, because the system is not just answering questions—it’s taking actions. It is helping managers interrogate data, update records, recommend actions, and potentially interact with external systems.
In practice, orchestration works best when responsibilities are clearly separated. If everything is handled in one place, it becomes difficult to understand what went wrong when something breaks. One part of the system interprets user intent, another decides how to handle it (knowledge base, tool call, or external integration), another validates whether the action should even happen, and another applies guardrails before the final response is returned. This is what makes the feature operationally useful while still keeping it controlled.
At a high level, the flow looks like this: the user submits a prompt, the orchestration layer classifies intent, guardrails evaluate safety and policy fit, the orchestrator selects the correct execution path, tools and knowledge sources are called where appropriate, results are validated, and then the final response is generated. If the request is sensitive, such as changing quantities, deleting menu options, or placing an order above a threshold, the workflow can pause for human review before the action is completed.
What’s important here is that each of these components is not just a technical decision—it directly supports one or more tenets of Responsible AI.
| AI Component | Purpose | Responsible AI Tenet |
|---|---|---|
| System Prompt | Defines the instructions, policies, and persona that guide how the AI behaves across all interactions. | Governance, Safety, Transparency |
| LLM (inbound) | Interprets the user input, extracts intent, and determines how the request should be handled within the system. | Explainability, Veracity |
| Guardrails | Evaluate the request for policy compliance, harmful content, prompt injection attempts, and unsafe behavior. | Safety, Governance |
| Orchestrator | Selects the correct workflow path and coordinates tool usage, retrieval, and approval steps. | Controllability, Governance |
| Knowledge Base / RAG | Provides grounded responses for how-to questions and policy guidance. | Veracity, Explainability |
| Internal Tools | Query and update inventory, menu, and order data. | Governance, Privacy & Security |
| External Connectors | Integrate with supplier platforms through MCP (or similar) to check stock and place orders. | Privacy & Security, Governance |
| Human-in-the-Loop | Introduces approvals for sensitive actions or uncertain outcomes. | Controllability, Governance |
| LLM (outbound) | Generates the final response using validated data, tool outputs, and orchestration context while maintaining safe and grounded behavior. | Veracity, Safety, Explainability |
| Observability | Captures traces, tool calls, approvals, feedback, and errors for later analysis. | Explainability, Governance |
The important idea here is that each of these components is not just a technical decision. Each directly supports one or more tenets of Responsible AI.
Transparency can be addressed in the user interface by clearly stating that the user is interacting with AI. Fairness is addressed by testing the LLM for bias across personas and situations. Safety is addressed by applying guardrails before and after model interaction. Explainability is addressed by retaining workflow traces, tool calls, and model context. Governance is addressed by assigning ownership, approval thresholds, and SLAs to the components involved in the workflow.
Testing Considerations
Traditional QA tends to focus on deterministic behavior: given the same input, does the system return the expected output? AI-powered features force a broader testing mindset. The feature may behave differently depending on phrasing, context, model updates, and retrieved data. That means QA needs to think not only in terms of expected functionality, but also in terms of Responsible AI failure modes.
This is where traditional QA approaches can break down. You’re no longer testing just functionality, you’re testing human-like behavior.
The starting point is to test ordinary business scenarios thoroughly. To this end, restaurant managers and other users should be able to ask How-To questions, query inventory, create or update orders, and receive clear, grounded responses. That’s just the baseline. The harder part is validating how the system behaves when prompts are ambiguous, emotional, malicious, irrelevant, or simply unexpected.
| QA Area | What to Test | Related Tenets |
|---|---|---|
| Toxic Content | Test prompts with profanity, insults, or emotionally charged input to verify that the chatbot remains professional. | Safety, Fairness, Veracity, Robustness |
| Prompt Injection | Test whether the model can be manipulated into ignoring instructions, exposing data, or calling unintended tools. | Safety, Robustness, Privacy & Security |
| Bias Testing | Test whether responses differ unfairly based on role, language style, cultural phrasing, or persona. | Fairness |
| Hallucination / Grounding | Test whether responses stay faithful to tool output and retrieved knowledge rather than inventing facts. | Veracity, Explainability |
| Role-Based Access | Test whether only authorized users can change menu options, ingredient mappings, and sensitive records. | Privacy & Security, Governance |
| Human-in-the-Loop Paths | Test approval workflows for high-risk actions and validate that rejection and override paths behave clearly. | Controllability, Governance |
| Feedback Capture | Test whether thumbs up/down and related comments are captured accurately and tied to the correct trace. | Transparency, Explainability |
QA also has to think about variability. The same question may be phrased in multiple ways, sometimes politely and sometimes not. Consider the situation from the previous story when Solène prompted the chatbot using "choice French idioms" and the chatbot generated a response that left her feeling hurt. The system needs to remain professional and bounded across those variations. This is the shift: QA is no longer just validating feature correctness. It is validating the conditions under which the AI remains safe, explainable, fair, and reliable.
Testing an AI-powered feature is not only about proving that it works. It is about proving that it continues to behave responsibly when real people interact with it in unpredictable ways.
Ongoing Monitoring
Launch is not the end of the work. If anything, it’s where the real work begins. This is where Responsible AI becomes visible to the business. Once real users start interacting with the system, the team needs to monitor more than just feature adoption, but also behavioral drift, model quality, feedback signals, and workflow failures.
Human feedback creates the learning loop for the product and development teams responsible for improving the feature. Observability makes it possible to understand what actually happened when something goes wrong. Without those two capabilities, the product and development teams are left guessing why it happened.
| Monitoring Area | Why It Matters |
|---|---|
| Prompt Patterns | Identify how users are actually phrasing requests and where intent classification needs refinement. |
| Guardrail Events | Track how often harmful or disallowed inputs are detected and whether policies need tuning. |
| Tool Usage | Monitor which tools are being called, whether they succeed, and where failures occur. |
| Approval Rates | Understand how often the user agrees with the response: content, data accuracy, context, etc., to demonstrate trust in the AI. |
| Disapproval Rates | Identify how often responses do not agree with the user's expectations which can, in the case of responses that are considered offensive, inaccurate, etc., a lack of user trust in the system. |
| Feedback Signals | Use thumbs up/down and comments to identify weak workflows or poor response quality. |
| Response Drift | Watch for changing model behavior over time as prompts, usage patterns, or models change. |
| Incident Analysis | Support root cause analysis when a user reports harmful, inaccurate, or unprofessional behavior. |
Implementing observability and reviewing the monitoring data regularly, pivots the tenets of Responsible AI from a product workflow to an operational tool for product improvement. It is where Explainability becomes traceability, Governance becomes ownership, and Safety becomes an active control rather than a design assumption. Teams that take monitoring seriously are far more likely to identify unexpected and unacceptable behaviors of the AI early, before they become failures that eventually surface publicly. Go back and review the failures listed in the first blog. Can you identify how the Tenet of Responsible AI could have improved the user experience for those failures?
Responsible AI is not something you add at the end. It is something you design for from the beginning. It shapes the use cases, requirements, architecture, QA strategy, and monitoring model of the feature. The more operational the AI becomes, the more important those controls become.
Bringing this back to Lattice Culinary Systems: applying these tenets and design choices is what allows the team to move from a fragile prototype to a reliable product. With clearer use cases, mapped risks, controlled workflows, and strong monitoring, the chatbot is no longer guessing—it is operating within defined boundaries. That is what gives the team confidence to go back to Maison Verdan not just with a fix, but with a system that can be trusted during peak service.
Up Next: In the next post, I will build on this product example and walk through how to design a Product Owner dashboard for an AI-powered feature. The focus will be on what to measure, how to surface signals like drift, feedback, and failures, and how product teams can use those insights to continuously improve the system.