ADAB: AI-Driven App Building
Part 1, Background and Introduction
This article marks the beginning of a series that unveils a groundbreaking technology developed at Apsy, reshaping our expectations for app and software creation. In Part 1, we explore foundational concepts and alternative methodologies, setting the stage for a deeper dive into ADAB in the following installment.
Introduction
At Apsy, we've introduced the concept of AI-Driven App Building, abbreviated as ADAB. What does AI-Driven App Building (ADAB) truly encompass? This question forms the cornerstone of our exploration in this article. Understanding the mechanics behind app creation is pivotal in a rapidly evolving digital landscape. While advanced, the current state of AI in app generation often relies on significant human intervention for decision-making. It needs a more nuanced understanding of a user's non-technical requirements. Our exploration seeks to bridge this gap, aiming for a fully autonomous and intuitive AI-driven process.
Figure 1 illustrates this process: it begins with the builder’s vision and culminates in generating functional app source code. Unlike a linear pathway, app building is an iterative journey. The builder often starts with a high-level concept, primarily business-focused, with many details yet to be uncovered. Figure 1 shows two loops involved in the iterative process:
The feedback loop, shown as a red cycle, represents what has been built.
The clarification loop, shown as a blue cycle, asks the builder to clarify the gaps.
Figure 1. The iterative nature of the app-building process
These nuances emerge as we delve deeper. Hence, the elicitation of details must be both iterative and interactive. Iterativity indicates that understanding unfolds progressively, not all at once. Each round of questions answered by the builder paves the way for further inquiries. Interactivity is crucial, too. The builder needs to visualize the app's current state to inform their responses effectively. It's unrealistic to expect a complete mental model of the app without seeing its evolving form.
With this understanding, we can now define ADAB: The process wherein an AI agent - hereafter referred to as the agent - uses the information from each iterative cycle to produce an interim app version and formulate subsequent queries. As shown in Figure 2, this cycle repeats until the app reaches fruition. As we go through the spiral loop, ADAB identifies gaps, interacts with the builder to clarify and remove the gaps, generates more features, and gets the builder closer to being fully satisfied with the outcome.
However, this definition hinges on several assumptions:
Near-Perfect Language Understanding and Artifact Creation: The agent's ability to comprehend language nearly perfectly and proficiency in creating artifacts are foundational.
Completion Criteria: An application is considered complete when the builder declares it is ready and the agent confirms it is devoid of technical issues. This subjective measure of completion emphasizes the importance of the builder's satisfaction with the application's functionality and performance, ensuring that the final product aligns with their initial vision.
Visual and Functional Representation: The agent's ability to generate a visual and functional depiction of the app at any development stage is crucial. This feature facilitates a tangible feedback loop, allowing the builder to make informed decisions and provide specific feedback based on the app's current state rather than relying on abstract concepts.
Non-Technical Builder Profile: We assume that the builder does not have any technical knowledge. Consequently, the agent is designed to avoid posing technical questions throughout development. This assumption ensures that the AI tailors its interactions and queries to the builder's level of expertise, focusing on functional and business-oriented aspects rather than technical details.
Figure 2. Spiral nature of ADAB
While contemporary LLM technologies like GPT meet some of these assumptions, others are integral to our proposed solution, which we will discuss in the Problem Section.
We consider Socient, a conceptual social networking app, to ground our discussion in reality. In the case of Socient, ADAB would facilitate the creation of its unique features, such as live events and AI moderation, by iteratively refining the app's functionalities based on the builder's feedback and the evolving understanding of user interactions within the app.
Listing 1. Running Example: Socient
Socient is a social networking application designed to enrich user interactions through live events. This platform integrates the core functionalities of social networking, such as posting, commenting, and connecting with friends. Unique to Socient is its feature that enables users, referred to as "posers," to organize live events for individuals who have actively and constructively engaged with their posts.
Socient leverages AI during these live events to moderate discussions, ensuring that conversations remain focused and respectful. The AI moderation guides the discussion to adhere to a predetermined train of thought, preventing insult and diversion from the topic and ensuring equitable participation among all attendees.
Post-event, the AI system synthesizes the discussion points into a concise summary, suggesting it as a post for participants to share on their respective pages. Furthermore, Socient employs a sophisticated algorithm to evaluate users' engagement levels and the quality of their contributions. This evaluation is quantified into a score that influences the visibility of users' future posts on the platform, promoting a culture of meaningful and constructive interactions.
Problem
The central challenge in this article is eliciting necessary information from the builder and its transformation into a functional application through ADAB. This task encompasses several critical challenges:
Interpretation of the Builder’s Expectations: How can we accurately understand and interpret the builder's vision for the app?
Conflict Resolution: How do we identify and reconcile the builder's conflicting expectations?
Transformation into Artifacts: What is the process for converting the builder's expectations into various app artifacts?
Gap Identification: How are gaps in the provided information detected, and what strategies can be employed to fill these gaps?
Questioning Strategy: How can we formulate clear, concise questions to elicit additional necessary information without overwhelming the builder?
Figure 3 shows the questions we have tried to address regarding these challenges.
Figure 3. Problem Challenges
The initial step involves engaging the builder in a dialogue to outline their app concept. For instance, in the context of Socient, the dialogue might initiate as follows:
A: Please describe the app you envision.
B: I need an app facilitating social interaction and engagement through live events.
A: Could you elaborate on the types of events you're considering?
B: Ideally, users could meet in person if proximity allows or engage through online events.
Responses from builders can vary widely, ranging from detailed descriptions akin to Listing 1 to concise or even unrelated responses. The AI agent's challenge is to navigate these responses effectively, extracting relevant information while minimizing divergent dialogue. Key agent characteristics essential for this process include:
Smooth Conversation Management: The agent should maintain a focused conversation, minimizing off-topic diversions.
Efficient Questioning: The agent should aim to ask the fewest possible questions to fulfill its objective.
For example, concerning Socient, the first two challenges are:
Interpretation: Should the initial description be interpreted strictly as a social app, or should assumptions be avoided?
Conflicts: The mention of live and in-person events introduces potential conflicts in the app's scope.
To contextualize these challenges, it's important to note that existing methodologies in tool-driven development, such as low-code or no-code tools, have primarily focused on technical aspects, often overlooking the nuanced expectations of non-technical builders. Our approach seeks to bridge this gap, offering a more intuitive and user-centric development process. The novelty of our solution lies in its ability to seamlessly integrate the builder's vision with AI capabilities, ensuring a smooth transition from concept to prototype without necessitating deep technical knowledge on the builder's part.
Understanding and implementing the builder's vision in a way that respects their expertise level is crucial, emphasizing the need for an AI system that adapts to varying degrees of technical fluency. Successfully addressing these challenges could significantly democratize the app development process, making it accessible to a wider range of creators and potentially accelerating innovation in the app market.
Addressing these challenges demands a sophisticated solution that leverages LLMs, a nuanced representation of app requirements, a comprehensive feature repository, and intricate algorithms. These elements work together to generate a dynamic app state, facilitating assertions and progress towards a fully realized app. The subsequent sections will delineate our proposed solution and demonstrate its capability to navigate the outlined challenges.:
Alternative Approaches
While we pioneer a fully AI-driven approach to app development, it's important to acknowledge existing alternative methods that assist in app building. This section briefly reviews these methods, setting the stage for our innovative solution in the next section.
Large Language Models (LLMs)
Despite common perceptions, the standalone capability of LLMs in app development is limited. Here's why:
Cohesive App Representation: LLMs lack a unified structure to link an app's linguistic expectations efficiently with its structural components, such as screens and APIs. Without this, managing an app's complexity becomes challenging.
Real-Time Feedback Loop: Text-based UIs of LLMs fall short in providing the rich visual feedback necessary for app refinement.
Command-Driven Nature: LLMs primarily respond to direct commands, often reverting to this mode after a few interactive cycles. Their suggestions are based more on general linguistic knowledge than on specific app goals.
Building Blocks for App Development: While LLMs can access public source code, they struggle with integrating complex features into an app cohesively, often resorting to placeholders instead of actual implementation.
Interpretation and Application: LLMs are not adept at fully interpreting user expectations or applying them contextually within the app development process.
While LLMs can't autonomously build apps, they can accelerate development by generating code for specific functionalities, significantly reducing coding time for developers.
Low Code/No Code (LCNC) Tools
LCNC tools aim to simplify app development but come with their own set of challenges:
Technical Knowledge Requirement: Despite their intuitive interfaces, LCNC tools require users to understand various technical aspects of app building.
Extensive Learning Curve: Learning to use LCNC tools effectively can take a significant amount of time, often months, which can be impractical for many users.
Time-Intensive Development Process: Building an app with LCNC tools can take several months, even for trained users.
Quality and Creativity Limitations: The generic nature of LCNC tools often results in less distinctive and lower-quality apps.
Scalability and Adaptability Issues: LCNC-built apps can be challenging to scale or adapt based on user feedback and evolving needs.
While LCNC tools democratize certain aspects of app development, their limitations in flexibility, creativity, and technical demand present significant barriers.
Template-Based and Off-The-Shelf Approaches
These tools offer generic templates or ready-to-use solutions for various app types, such as e-commerce. However, they fall short in several areas:
Customization and Branding Limitations: High customization needs and distinct branding requirements are often beyond the scope of these tools.
Ownership and Marketing Challenges: These approaches are often inadequate for unique app branding and ownership, offering minimal customization and branding opportunities under a generic app name.
While offering quick deployment, template-based and off-the-shelf solutions lack the versatility and personalization necessary for many app projects.
In summary, while these alternative approaches provide certain benefits, they fail to fully address app builders' nuanced and evolving needs. Our AI-driven solution, as detailed in the following section, seeks to overcome these limitations by offering a more adaptive, intuitive, and comprehensive approach to app development.
The Solution: Xi
Xi represents Apsy's advanced iteration of the ADAB paradigm. While the details of Xi are secret and being patented by Aspy, it suffices for this article to inform the reader that Xi encapsulates the following core attributes: automation, smooth interaction, intuitive operation, and elegance.
Fully Automated Process: Xi leverages sophisticated algorithms and intelligent features to create a fully automated development journey, eliminating human intervention. This approach not only accelerates the process but also minimizes errors and allows for scalability without additional costs.
This means “we don’t have a person who punches on the keyboard in the background!”
Smooth Conversation: Xi is engineered to transcend the typical chatbot experience, aiming to provide interactions as engaging and informative as those with a team of human experts. Our goal is to ensure that every exchange feels natural and enriching. This means that “we never tell the builder, I did not understand that, please rephrase!”
Intuitive Flow: Conscious of the builder's expertise level, Xi intentionally avoids technical jargon, keeping the dialogue focused on business-related discussions. By minimizing the number of questions asked and proactively offering recommendations, we streamline the development process for an optimal user experience.
This means “we don't ask the builder to tell us how to integrate with an API!”
Elegant Outcome: The end product of Xi's development process is designed to meet or exceed the quality standards of the top apps in any given vertical. Our commitment is to deliver apps that are not only functional but also visually appealing and user-friendly.
This means “nobody can tell the app has been built by a tool!”
What Will Come Next
In upcoming articles, we will delve into the deployment of Socient utilizing a unique LCNC methodology alongside ADAB, demonstrating how ADAB transforms app development into an enjoyable and straightforward process.