Unlocking the Future: Advancing AI from Gen 1.5 to 2.0 with Cutting-Edge Agent Systems

With over a year of developing solutions based on generative AI foundation models, the focus has now shifted towards multi-modal models capable of handling images and videos, making the term “foundation model” more suitable than large language models (LLMs).

The world is currently experiencing the emergence of patterns that can be harnessed to effectively implement these solutions and meet the diverse needs of individuals. There are transformative opportunities on the horizon that will allow for more intricate uses of LLMs, but these opportunities come with increased costs that must be managed.

Understanding the functionality of foundation models is essential. These models convert words, images, numbers, and sounds into tokens and predict the next best token based on user interaction. Through continuous learning and feedback, core models from various organizations have become more aligned with user expectations.

Exploring the conversion of language to tokens has revealed that formatting plays a crucial role, with YAML outperforming JSON. The generative AI community has developed “prompt-engineering” techniques to enhance model responses effectively.

For example, providing a few instances (few-shot prompt) can steer a model towards the desired response style. By breaking down problems (chain of thought prompt), models can generate more tokens, increasing the likelihood of arriving at correct answers to complex questions. Users of consumer gen AI chat services have likely observed these improvements over the past year.

Gen AI 1.5: Enhancing Retrieval, Embedding Models, and Vector Databases

Advancements in LLM capabilities have enabled models to process up to 1 million tokens, allowing users to control context when answering questions in ways previously unattainable. Complex legal, medical, or scientific texts can now be queried with high accuracy using LLMs.

Moreover, technology leveraging LLMs to store and retrieve text based on concepts rather than keywords is expanding. New embedding models and vector databases are facilitating the retrieval of similar text from diverse sources, enabling scalability to millions of documents with minimal performance impact.

Scaling these solutions in production remains a complex task, necessitating collaboration across various disciplines to optimize system performance in areas such as security, scaling, latency, cost efficiency, and data quality.

Gen 2.0 and Agent Systems

While improvements in model and system performance are enhancing solution accuracy, the next phase involves creatively combining different gen AI functionalities. Agent-based systems utilizing multi-modal models and a reasoning engine are paving the way for more flexible and complex solutions.

Tools like devin.ai and Amazon’s Q for Developers are streamlining tasks such as programming language changes and design pattern refactors with minimal human intervention. Medical agent systems are leveraging data from various sources to provide detailed responses and recommendations for clinicians.

However, without optimization, these systems can be costly to operate due to the high volume of LLM calls. Ongoing developments in LLM optimization techniques are crucial to mitigate costs and enhance efficiency.

In Summary

As organizations progress in their utilization of LLMs, the emphasis will be on delivering high-quality outputs quickly and cost-effectively. Collaborating with experts experienced in running and optimizing gen AI solutions in real-world scenarios will be vital for success.

Ryan Gross is senior director of data and applications at Caylent.