A Better Way to Build AI Products - Lessons from Visual Co-Pilot

Video Summary

The video discusses the issues with the common approach of building AI products by simply wrapping existing large language models (LLMs) like ChatGPT. The speaker, Steve, explains that this approach leads to lack of differentiation, high costs, and poor performance.

Key Points:

1. LLM wrappers are easy to copy, making it difficult to differentiate your product.
2. Running LLMs is incredibly expensive, often costing more than what customers are willing to pay.
3. The inherent slowness of LLMs can be a major bottleneck for certain use cases.

Insightful Ideas:

1. Building custom AI solutions by combining LLMs with other specialized models and custom-coded logic can result in faster, cheaper, and more differentiated products.
2. Using AI/ML only for the most difficult parts that can't be solved with code alone is a more effective approach.

Actionable Advice: Ditch the common LLM wrapper model and embrace a more custom, hybrid approach to building AI products. This can help you create unique, high-performing solutions that stay ahead of the competition.

Ditch the LLM Wrapper: A Better Approach to Building Unique, High-Performing AI Products

In the world of AI product development, there's a common pattern that many are falling into - simply wrapping existing large language models (LLMs) like ChatGPT to create new applications. While this approach may seem easy and tempting, it often leads to a host of issues that can undermine the success of your AI product.

Steve, the creator of the Visual Co-Pilot tool, provides a better alternative in this insightful video. Instead of just relying on LLM wrappers, he recommends building custom AI solutions that combine LLMs with other specialized models and custom-coded logic. This approach, he argues, can result in faster, cheaper, and more differentiated AI products that are truly unique in the market.

The key problems with the LLM wrapper approach, as Steve explains, are lack of differentiation, high costs, and poor performance. When everyone is just calling the same underlying model via an API, it becomes easy for competitors to copy your work. Additionally, the large and complex nature of LLMs makes them incredibly expensive to run, often costing more to operate than what customers are willing to pay. And for certain use cases, the inherent slowness of LLMs can be a major bottleneck.

To overcome these challenges, Steve advocates taking a different path - one that involves a combination of LLMs and other specialized AI/ML models, along with custom-built algorithms and logic. This approach, used in the development of Visual Co-Pilot, allows for greater control, customization, and optimization of the AI system.

Rather than just passing design files into a single LLM and waiting for the output, the Visual Co-Pilot team broke down the problem into smaller, more manageable pieces. They used regular programming techniques to handle as much of the conversion process as possible, only relying on AI/ML models for the most difficult tasks that could not be solved through code alone.

This hybrid approach enabled them to build a fast, cost-effective, and highly differentiated product. The custom-trained models and tightly integrated components allow for continuous improvements, ensuring the product stays ahead of the competition. Additionally, the ability to ensure strict privacy and security controls is a major advantage, especially for enterprise customers.

Steve's insights provide a valuable lesson for anyone looking to build successful AI products. By thinking beyond the LLM wrapper and embracing a more custom, hybrid approach, you can create solutions that are truly unique, high-performing, and poised for long-term success.