Unleash Your AI Potential: Avoid the Common Path

Table of Contents

1. Introduction
2. The Problem with Current AI Product Development
3. The Issues of Non-Differentiated Technology
4. The Problem of Cost in AI Product Development
5. The Challenge of Slow Performance in AI Products
6. The Limitations of Customization in Language Models
7. An Alternative Approach to AI Product Development
8. The Importance of Normal Coding in AI Solutions
9. Leveraging Specialized AI Models for Specific Problems
10. Generating Data for Training Custom AI Models
11. Connecting AI Models to Code for Improved Functionality
12. The Advantages of Owning and Improving Your Own Models
13. Privacy Considerations in AI Product Development
14. Conclusion

An Alternative Approach to AI Product Development

AI products have gained immense popularity in recent years, with numerous applications being developed using pre-trained models like chat GPT. However, this approach has significant drawbacks that can hinder the uniqueness, value, and speed of AI products. In this article, we will explore an alternative approach to building AI products that address these problems and deliver better results.

Introduction

The current trend in AI product development involves using pre-trained models and wrappers to Create applications quickly. While this approach may seem easy and efficient, it often leads to non-differentiated technology and a lack of customization options. Additionally, the costs associated with running large language models and the slow performance they exhibit pose significant challenges. In this article, we will discuss the issues with the current approach and present an alternative method that offers improved differentiation, cost-effectiveness, and performance.

The Problem with Current AI Product Development

The Issues of Non-Differentiated Technology

Many AI products in the market today lack differentiation due to their reliance on pre-trained models and standardized techniques. When multiple developers use the same models and mimic existing applications, their products fail to stand out. This lack of differentiation makes the product easily replicable by competitors, leading to a highly risky position for developers.

The Problem of Cost in AI Product Development

Another major issue with the current approach to AI product development is the high cost associated with running large language models. While these models are versatile, their complexity and size make them expensive to operate. This cost is often passed on to the users, which can result in a misalignment between what users are willing to pay and the actual cost of the service. Additionally, most AI use cases do not require models trained on the entirety of the internet but rather specific domains Relevant to their application.

The Challenge of Slow Performance in AI Products

Large language models (LLMs) used in AI products often suffer from slow performance, which can limit their usability in certain applications. While the speed of response may not be critical in some cases, it becomes a significant problem when applications require the entire response before proceeding to the next step. For example, in building a visual co-pilot product, the conversion from design to code using an LLM took an unacceptably long time, making it impractical for efficient workflows.

An Alternative Approach to AI Product Development

To overcome the limitations of the current approach, developers should consider a different methodology for building AI products. Instead of relying solely on pre-trained models, the recommended approach involves creating a toolchain that combines fine-tuned LLMs, other technologies, and custom-trained models. Contrary to popular belief, training your own models is not as challenging as one might think, and even moderately experienced developers can accomplish it.

The Importance of Normal Coding in AI Solutions

To start building AI products, it is crucial to first explore the problem space using traditional coding practices. This initial step helps identify areas that can benefit from specialized AI models and determine the most effective use of AI technology. Building supermodels that can handle complex problems end-to-end with a single model is often not the right approach.

Leveraging Specialized AI Models for Specific Problems

Instead of relying on one all-encompassing AI model, developers can create a combination of specialized models and connect them with regular code. These specialized models can be trained to perform specific tasks that are challenging for traditional code alone. For example, in the visual co-pilot product, multiple models were used for tasks like object detection, transforming designs to responsive code, and generating data.

Generating Data for Training Custom AI Models

One challenge in training custom AI models is acquiring the necessary data. Fortunately, the internet serves as a vast resource for generating data. Techniques such as web scraping and automating web browsers can be used to Collect screenshots and extract vital information required for training the models. Creative approaches to data generation can greatly enhance the effectiveness and accuracy of AI models.

Connecting AI Models to Code for Improved Functionality

To create a seamless integration between AI models and code, developers should strategically connect the models only at the points where they are needed. By selectively using AI models for critical tasks, developers can harness the benefits of AI while optimizing performance and maintainability. This approach ensures that the AI technology enhances the overall functionality of the product without introducing unnecessary complexity.

The Advantages of Owning and Improving Your Own Models

One significant AdVantage of the alternative approach is the ownership and control over the models used in the product. By owning the models, developers have the freedom to continually improve and fine-tune them Based on user feedback and evolving requirements. Unlike relying solely on external models, developers have complete control over the performance, privacy, and customization of the models, enabling them to deliver superior products.

Privacy Considerations in AI Product Development

In the era of heightened data privacy concerns, it is essential to address these issues in AI product development. By owning the entire technology stack and models used in the product, developers can ensure stringent privacy standards are met. For privacy-conscious organizations, the ability to disable certain models or integrate in-house built models provides reassurance that sensitive data remains secure.

Conclusion

The traditional approach of building AI products by wrapping pre-trained models often results in non-differentiated technology, high costs, and slow performance. To overcome these challenges and develop unique, valuable, and fast AI products, developers should consider an alternative approach. By combining fine-tuned models, specialized AI solutions, and regular code, developers can create products that are more cost-effective, performant, and customizable. This approach allows for continuous improvement, enhanced privacy, and differentiation in a rapidly evolving AI landscape.

Highlights

1. The current approach to AI product development lacks differentiation and offers limited customization options.
2. Running large language models can be costly and result in a misalignment between cost and user willingness to pay.
3. Slow performance of large language models can hinder the usability of AI products.
4. An alternative approach involves creating a toolchain combining fine-tuned models, custom-trained models, and other technologies.
5. Using normal coding practices helps identify specific problem areas where AI models can be beneficial.
6. Specialized AI models can be leveraged to perform complex tasks that traditional code struggles with.
7. Creative data generation techniques, such as web scraping, can enhance the effectiveness of custom AI models.
8. Connecting AI models to code strategically enhances product functionality without introducing complexity.
9. Ownership and control over models allow for continuous improvement and customization.
10. Privacy considerations can be addressed by owning the technology stack and integrating in-house models.