Leveraging AI to improve content summarization and learning

Redefining Digital Content Consumption with AI

Knowledge is power, but too much information can be overwhelming. Taim aims to simplify the complex landscape of digital content. Our mobile app uses AI to summarize articles and YouTube videos, creating digests in three customizable formats.

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Taim is our internal R&D project to explore the potential of language learning models in everyday user scenarios and contexts.

The goal behind Taim was to help users consume and manage information more effectively. From the start, we were designing Taim to maximize user experience.

Project assumptions

  • Users value efficiency and prefer not to be overwhelmed with excessive information.
  • Native sharing capabilities to let Taim work across various platforms and tools (sharing a link directly to Taim to get a summary), compatible with Chrome, YouTube, Safari, and other popular applications.
  • Users may want summaries in varying lengths and forms based on their specific needs.
  • A multi-language feature to broaden the app's user base.
  • A simple user-based rating system for feedback and product iterations.
  • Data privacy is a critical concern for most users today. No need to set up an account.
  • Design should be minimalist, to focus on the most effective user experience.

Scope of Work

Scope of work for Taim included the development of AI-powered mobile apps for efficient digital content summarization, ensuring ethical compliance and continuous improvement through prompt evaluation and user feedback mechanisms.

Mobile App Development for iOS and Android

Utilizing Flutter for developing user-friendly, cross-platform mobile applications, focusing on providing a seamless experience in content summarization.

AI Integration and Backend Development

Implementing cutting-edge AI technologies, including OpenAI's ChatGPT, using LangChain and Helicone for efficient language model interaction, and developing a robust backend with Fastify to manage complex AI interactions.

Content Moderation and Ethical Compliance

Incorporating a content moderation step in the app to ensure ethical compliance, leveraging AI to flag and filter inappropriate content.

Prompt Crafting and Quality Evaluation

Developing and refining AI prompts using an internal tool built with Retool, enabling the team to evaluate and improve the accuracy and usefulness of AI-generated summaries.


Step 1: Working on architecture

When it comes to building a mobile application that relies on cutting-edge AI capabilities, the choice of technologies is paramount. Here's how we approached the architecture of Taim.

Multi-platform availability

Taim is not just another mobile application — it's a comprehensive solution that consists of:

  • Mobile app for iOS: Designed to leverage Apple's ecosystem, providing a seamless experience for iPhone users.
  • Mobile app for Android: Engineered to be fast, responsive, and intuitive across a variety of Android devices.
  • Backend app: Serves as the bridge that facilitates communication with language learning models (LLM).

Choosing Flutter for mobile frontend

We chose Flutter for both the iOS and Android mobile apps. This decision allowed our development team to be more agile, focusing on feature-rich development and rapid delivery. Flutter has been an instrumental choice right from the prototyping phase through development. The framework enabled us to maintain a smaller, more focused team without compromising on quality or speed.

Using Fastify for backend

Our backend application is crafted using Node.js with Fastify as the web framework. Fastify is designed for efficiency and a low learning curve, enabling us to build a robust backend that can handle the complexities of interacting with AI tools while being easy to maintain.

The LangChain and Helicone Combo for LLM

Interfacing with Language Learning Models (LLM) like OpenAI requires specialized tools. We leveraged two primary frameworks for this:

  • LangChain: A comprehensive toolset that has become a standard in developing applications based on LLMs. It streamlines the development process, allowing us to focus on user experience and core functionality.
  • Helicone: This is our go-to monitoring tool for LLM. It lets us keep tabs on costs, monitor prompts, and even implement caching for repeated articles. This real-time insight is crucial for maintaining the efficiency and cost-effectiveness of Taim.
The current stack view of the Taim app

The blend of these technologies ensures that Taim is not just another app but a well-thought-out solution designed to improve the way we consume information.

Step 2: Navigating the unpredictable: building products with AI

Incorporating AI into mobile applications brings a new layer of complexity, especially when dealing with non-deterministic technologies like language learning models (LLMs). Non-determinism, in the context of LLMs, means that the model can produce different outputs even when given the same input. This adds an extra layer of unpredictability that developers need to account for. While the possibilities are vast, so are the responsibilities.

Step 3: Moderating content for ethical compliance

AI, especially when interacting with external, user-generated content, can sometimes produce unexpected or inappropriate outputs. To mitigate this risk, Taim incorporates a text moderation step before summarization. We utilize a dedicated endpoint specifically designed to flag any content that violates ethical guidelines, such as hate speech or other inappropriate content. This ensures that the summaries generated align with societal norms and ethical considerations. We recommend starting with OpenAI's guidelines, which lays out comprehensive safety practices.

Step 4: The art of crafting prompts

Prompts in AI-driven applications are far from typical code fragments — they’re the questions that tell AI what you want from it. A well-crafted prompt can yield highly usable and accurate outputs, while a poorly-designed prompt can produce irrelevant or incorrect information. Throughout the development process, we experimented with dozens of different prompts for Taim's summarization feature.

To fine-tune this aspect, we developed an internal tool using Retool, which let us rate the quality of generated prompts and their respective summaries. Each team member could provide their subjective feedback, which was then compiled to identify which prompts resulted in the most accurate and useful summaries. This iterative, human-in-the-loop approach was invaluable for refining Taim’s AI components.

A screen from the no code solution to evaluate the quality of the generated prompts

We're excited about what taim can offer, and we believe it’s a great way to get the most valuable insights in much less time. If you're interested in gaining access to Taim, we'd be delighted to share it with you — just reach out.

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