DocAssist:
Project Documentation Assistant

[TRANSFORMING PROJECT DOCUMENTATION THROUGH CONVERSATIONAL AI]

*The following case study has been modified to protect the innocent (and the confidential). Any similarity to actual products, living or dead, is purely coincidental – but the design process and learnings are absolutely real.

Overview

The Challenge

Documentation is often seen as a challenging and overwhelming task, especially when trying to capture complex projects and decision-making processes. Users struggle to structure their thoughts and often miss important details when faced with traditional documentation methods. How might we make documentation more intuitive and comprehensive by leveraging natural conversation?

The solution

Project Documentation Assistant - An intelligent system that transforms documentation into an engaging dialogue. Through natural conversation, the platform helps users recall and articulate their process, decisions, and insights with greater clarity and completeness. Rather than facing blank forms, users are guided through a dynamic exchange that helps them build comprehensive documentation while maintaining a clear structure that can be easily reviewed and refined.

See solution

role and context

AI Product Designer
Discovery Design for Client Project
Duration: 2 months (2024)

My Contributions

Developed a conversational interface that transforms form-filling into natural dialogue
Designed progress tracking and completion guidance systems
Developed real-time feedback mechanisms and systems for identifying missing critical information.
Collaborated with data scientists to translate their conversation patterns and question-answer flows into an intuitive interface

Problem Discovery

Fascinated by how users naturally interact with AI, I conducted a data analysis exercise to understand prompt patterns and user behavior. Using Jupyter Notebook, Python, NLTK, and Pandas, I analyzed how users formulate their queries and how this could inform better documentation interfaces.

Research Question

How do users naturally structure information when interacting with AI chat interfaces, and how can this insight improve the way we present AI-transformed content back to them?

Data Collection

I analyzed 52,000+ prompts from the ShareGPT52K dataset to understand natural user behavior and communication patterns with AI systems.

Visualization Techniques

I created histogram visualizations using Matplotlib to represent:

Distribution of prompt lengths
Frequency of different prompt sizes
Basic statistical properties of the dataset

BASIC ANALYSIS

Prompt Length Analysis

A simple statistical analysis revealed interesting characteristics:

Average Length: Approximately 10 words per prompt

Median: 10 words

Range: 4 to 84 words

Typical Prompt Size: 8-12 words

Users typically write concise prompts, often gravitating towards 'how-to' questions. However, there's a tendency to include unnecessary context, resulting in prompts longer than needed—averaging around 42 words. This suggests users are still learning how to effectively communicate with AI, balancing between brevity and comprehensive explanation.

correlation Prompt Clarity and Response Quality

A simple statistical analysis revealed interesting characteristics:

Correlation Coefficient: -0.081 (very weak negative relationship)

Key Observation: Clearer prompts don't necessarily guarantee longer or more comprehensive responses

Key Insights

#1

Natural Communication Patterns

Users tend to be concise (avg 10 words) when unprompted, preferring direct communication over lengthy explanations.

#2

Follow-up Impact

Targeted follow-up questions significantly improve information quality and completeness.

#3

Clear Response Correlation

Clear prompts elicit more precise responses than open-ended questions.

#4

Quality Priority

Quality matters more than length in documentation - the relevance and precision of responses are more important than their size.

DESIGN IMPLICATIONS

Interface Design

Guide users with specific, targeted questions while maintaining conversation flow
Progressive disclosure to maintain focus and prevent overwhelming users

Information Architecture

Real-time feedback showing documentation structure and completion
Balance between conversational interface and structured output

user persona

Alex Rivera

Mid-Level UX Designer

"I've done great work on this AI project, but now I need to document everything for my portfolio. It's been a few months, and I'm struggling to piece together my process."

◎

Goals

Create a compelling portfolio piece
Accurately represent the design process
Showcase AI/UX expertise
Stand out in job applications

⚑

Pain Points

Lost some early project documentation
Memory of process is fading
Uncertain how to structure the case study
Overwhelmed by project complexity

✻

Current Approach

Reviewing old project files
Looking through meeting notes
Checking in different Figma files
Consulting with former team members

☺︎

Needs

Clear case study structure guidance
Help reconstructing design decisions
Portfolio best practices
Templates for process documentation

User flow

Design Process

As the sole designer on DocAssist, I led the design process from concept to completion, while collaborating closely with our CPO and Data Scientist to ensure business alignment and technical feasibility. Here's how I approached the challenge:

Design Principles

Based on these findings, I developed core principles to guide my design approach, which I refined through discussions with the CPO:

#1

Progressive Documentation

Instead of overwhelming users with a blank canvas, guide them through structured documentation.

#2

Flexible Narrative

Allow for non-linear documentation while maintaining coherent structure.

#3

Context Preservation

Help users capture and organize supporting materials.

#4

Intelligent Assistance

Leverage AI to prompt for important details users might forget.

Design evolution

Initial Chat Interface

I started with a focused exploration of natural conversation patterns with a simple chat-based interface. The initial design prioritized an approachable entry point where users could freely express their documentation needs. I crafted a welcoming message that outlined clear possibilities.

Smart Documentation

During the prototyping phase, I identified progress visualization as a critical component serving two essential purposes: helping users track missing information while making AI assistance transparent.

Through a side panel, users can see in real-time how their conversational inputs transform into structured documentation.

Template Integration

I identified a key opportunity to improve the entry point of the experience. Rather than having users start from scratch and relying on AI to determine their goals, I designed a dashboard that would serve as a structured starting point.