The thoughtbot team worked with a client that has stayed relevant and successful through the use of cutting-edge innovations.

By leveraging the latest technological advancements in artificial intelligence and machine learning, this client optimized their internal workflows to provide unique insights and deliverables at lightning speed. As a marketing agency in the healthcare space, they pride themselves on helping their clients create relatable brand experiences powered by generative AI.

As a large enterprise, the Healthcare Marketing Agency has hundreds of employees who need to create meaningful, accurate marketing content from complex research studies and other scattered, technical documentation. They edit and repurpose the content in various formats and collaborate on it with others across the organization. This process was time consuming.

The goal of this internal tool was to allow various teams to quickly generate text responses to improve the workflow and productivity of their role.

In order to create an effective solution, we first needed to understand the our target users. There were two distinct groups:

Group 1: The data team was a group of tech-savvy users already using ChatGPT who were comfortable with the way it works.

Group 2: The client-facing teams primarily work on creating marketing campaigns for pharmaceutical companies. This group was less tech-savvy.

In addition to understanding the users and their use cases, the thoughtbot team started running unit tests to fully immerse in the project. Running unit tests and understanding the bounds of OpenAI’s ChatGPT capabilities created a foundation from which to build.

Creating a superior, more relevant experience to ChatGPT and manual data analysis

The initial MVP of the internal tool was a wrapper around ChatGPT that focused on common tasks for the marketing team. For example, the common task of creating personas based on large data sets. With the new AI-powered internal tool, the marketer now has access to canned prompts that guide them through uploading a CSV and then prompting OpenAI for next steps such as "split into 5 market segments" then "turn segment 1 into a persona" and so on.

Designing a custom, simple OpenAI user experience

There were three design priorities:

1. Make the text input experience as simple as possible. This included how the user interacts with the selection of prompts and inputs the starter text.
2. Designing a user-friendly prompt experience that is accessible for less tech-savvy team members to navigate. thoughtbot included prompt functionality that streamlined making instant changes and conducting on-the-spot testing.
3. Creating overall consistency in the internal tool by using a shared visual components. This ensured the experience would feel cohesive and established a useful library for future development to maintain standards and develop quickly.

Tooling for both Web developers and data scientists

thoughtbot had to balance technical work between two teams: Rails Web developers and Python data scientists. The main LLM work made use of the LangChain Python library, but the user interacted with a Rails app using the latest Hotwire library.

To make this work we built a simple Python Web API that proxies LangChain functionality. Since this was predominantly network-bound, we made use of the async Python tooling and the Quart Web library. We exposed basic LangChain functions as we needed them, including chains, and then used this API from the Rails app.

By teaming up with thoughtbot, the Healthcare Marketing Agency was able to create and launch their internal tool vision.

We successfully created a custom solution that takes advantage of OpenAI’s benefits while limiting the downsides of a public tool. Our solution prevented the lack of data protection, created a tailored user experience, and improved workflows to create a transformational outcome for our client.

Because this is an internal tool, our team had direct access to all users and fast, informative feedback cycles. This enabled us to iterate on features week over week to speed up the product roadmap.

Some of the highlights from the first phase of this ongoing project include:

• Improved testing, test coverage, and test abstractions
• Cloning conversations, cloning prompts
• Re-generating ChatGPT output to avoid hallucinations
• Sanitizing the output to avoid popular AI vulnerabilities
• Onboarding and setup improvements
• UX improvements for starting new conversations
• Easier to understand what effects new prompts will have
• Project prioritization and project management flow
• Documentation to help users struggling with the app technicalities (ie liquid templates, and prompt engineering)

Our work on the generative AI project continues with the Healthcare Marketing Agency. As the product has matured and users found value, word spread quickly from team to team about its effectiveness and additional teams continue to adopt it. The power of the solution has also inspired exploration into how it can evolve to become a client-facing product.