CASE STUDY · ENTERPRISE WORK
AI Innovation Lab Platform
Developer Platform for LLM Testing & Prompt Evaluation
Redesigning an internal AI experimentation platform used by developers, prompt engineers, and product teams to test LLMs, compare outputs, evaluate document analysis, and scale reusable prompt workflows.
ROLE
Principal UX Designer / Design Lead
USERS
Developers, AI prompt engineers, product owners, and business stakeholders
PLATFORM
Enterprise web platform
FOCUS
LLM testing, prompt workflows, document analysis, output evaluation, self-service tooling
TOOLS
Figma, prototypes, workflow maps, stakeholder reviews, engineering collaboration
STATUS
Redesigned / productized self-service platform direction
CONFIDENTIALITY NOTE
Some enterprise work is recreated, abstracted, or generalized to protect confidential information. Case studies focus on the design challenge, workflow complexity, decision-making, and product outcomes rather than proprietary implementation details.
PLATFORM DESIGN CHALLENGE
This was not just an AI demo tool. It was a platform design challenge: how to make LLM experimentation, prompt iteration, document analysis, output evaluation, and reusable workflows understandable across developers, prompt engineers, product owners, and business stakeholders.
Situation
Business context
The AI Innovation Lab began as an internal development tool for testing LLMs and exploring Intelligent Document Processing use cases. As more teams and use cases were added, the tool became harder to navigate, harder to evaluate consistently, and less accessible for non-developer stakeholders.
User context
Developers, prompt engineers, product owners, and business stakeholders needed a clearer way to test models, refine prompts, upload documents, compare outputs, review confidence, and understand whether results met expectations.
System context
The platform needed to support technical experimentation while evolving toward a more productized self-service experience. The design had to make complex AI workflows understandable without hiding the technical details users needed to evaluate results.
Design Challenge
How might we redesign an internal AI experimentation platform so developers, prompt engineers, product teams, and stakeholders can test LLMs, evaluate outputs, and reuse prompt workflows with less friction and more confidence?
The tool had grown organically
As more use cases were added, the experience became convoluted and harder to navigate — making it difficult to understand where to start and what to do next.
Prompt testing lacked clear workflow structure
Users needed a more organized way to select models, manage prompt versions, run tests, review results, and compare outputs — without losing track of what they had already tried.
Evaluation needed to be easier to trust
Users needed visibility into confidence scores, pass/fail validation, logs, run history, and document analysis results to evaluate AI behavior with real confidence.
My Role
What I led
Led the UX redesign of the AI Innovation Lab platform — framing the work around reducing complexity and supporting repeatable AI experimentation. Partnered closely with development teams, facilitated stakeholder reviews with product, engineering, prompt engineering, and business partners, and helped shift the platform toward a clearer self-service experience.
What I designed
Platform information architecture, model selection workflows, prompt versioning patterns, test run flows, document upload and analysis experience, side-by-side output comparison, confidence score and pass/fail review patterns, logs, run history, and reusable prompt template concepts for self-service workflows.
Who I partnered with
Developers, AI prompt engineers, product owners, business stakeholders, engineering leads, Intelligent Document Processing teams, and platform and AI partners across the organization.
Making the Workflow Visible
Before redesigning the interface, I mapped the end-to-end AI experimentation workflow: how users selected a model, created or reused prompts, uploaded documents, ran tests, reviewed outputs, compared results, and decided whether a prompt or model response was successful.
Exploring the Experience
Platform IA
Organizing model selection, prompt templates, test runs, document upload, output review, and history into a clearer navigational structure.
Prompt Versioning
Exploring how users could create, edit, save, compare, and reuse prompt versions across test runs to support iteration without losing prior work.
Document Analysis Flow
Designing how users upload documents, run analysis, and review AI-generated or extracted outputs against expectations.
Side-by-Side Output Comparison
Exploring how to compare responses across models, prompt versions, or test runs in a single view to support faster evaluation.
Logs and Run History
Designing visibility into past runs, system behavior, and troubleshooting details to support diagnosis and confidence in results.
Self-Service Productization
Exploring how the platform could support product owners and business stakeholders without losing the technical depth that developers and prompt engineers needed.
The Solution
The redesigned platform clarified the AI experimentation workflow and made it easier for users to move from setup to testing to evaluation. The experience supported technical users while making the platform more accessible as a self-service tool for broader product and business teams.
FEATURE AREA
Model and Prompt Setup
A clearer setup flow helped users select a model, choose a prompt template, edit prompt content, and prepare a test run — reducing setup friction and ambiguity.
FEATURE AREA
Prompt Versioning
Versioning patterns helped users track prompt changes, compare variations, and reuse successful prompt structures across test runs.
FEATURE AREA
Document Upload and Analysis
A structured document analysis flow helped users upload test documents and evaluate AI-generated or extracted outputs in a consistent, comparable format.
FEATURE AREA
Output Comparison
Side-by-side result comparison helped users evaluate differences across prompts, models, and runs — making quality judgments faster and more reliable.
FEATURE AREA
Confidence and Pass/Fail Review
Review patterns helped users understand confidence scores, validate results, and determine whether outputs met expectations with a clear pass/fail signal.
FEATURE AREA
Logs, Run History, and Templates
Logs and run history supported troubleshooting and diagnosis, while reusable prompt templates helped scale repeatable workflows across teams.
Key Design Decisions
Design the workflow around experimentation
Make outputs easier to compare
Expose confidence, logs, and run history
Productize without oversimplifying
Impact
USER IMPACT
Reduced friction for developers, prompt engineers, product owners, and stakeholders testing AI document workflows — making the platform usable by a broader team without losing technical depth.
PRODUCT IMPACT
Helped evolve the AI Innovation Lab from a convoluted development tool toward a more productized self-service platform with a clearer experimentation workflow.
WORKFLOW IMPACT
Clarified model selection, prompt versioning, test runs, document analysis, output comparison, and evaluation workflows — reducing the steps between setup and insight.
PLATFORM IMPACT
Created reusable UX patterns for AI experimentation, prompt templates, run history, logs, and result validation that can scale across future AI product work.
Reflection
What I learned
AI experimentation tools need to support iteration, comparison, and troubleshooting as first-class parts of the experience — not afterthoughts layered on top of a basic form interface.
What I would improve
I would continue refining how logs, confidence scores, and evaluation history help users diagnose issues without overwhelming them with raw technical output — the signal-to-noise balance is hard to get right.
How this shaped my design approach
This work reinforced that developer and AI platforms need to expose complexity in structured, legible ways instead of hiding it entirely. Users don't need less information — they need better-organized information.
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