Developing Solutions with Generative AI Foundation Models
We are now over a year into exploring generative AI foundation models. Initially focused on large language models (LLMs), we now see the rise of multi-modal models that can understand and generate images and videos, making "foundation model" (FM) a more appropriate term.
As the field evolves, we are identifying patterns to effectively bring these solutions into production and create meaningful impact by tailoring information to meet diverse needs. Numerous transformative opportunities lie ahead that promise to enhance the complexity and value derived from LLMs, although these advancements will require careful cost management.
Understanding Foundation Models
To leverage FMs effectively, we must grasp their inner workings. These models transform words, images, numbers, and sounds into tokens, predicting the most relevant "next-token" to engage users. Feedback over the past year has refined core models developed by Anthropic, OpenAI, Mixtral, and Meta, aligning them closely with user expectations.
Recognizing the significance of token formatting has led to improved performance—YAML typically outperforms JSON. The community has developed "prompt-engineering" techniques to enhance model responses. For instance, utilizing few-shot prompting provides examples to guide the model’s output, while chain-of-thought prompts can lead to more thorough answers for complex queries. Many active users of generative AI chat services have likely noticed these enhancements.
Advancements in LLM Capabilities
Expanding the information processing capacity of LLMs is foundational to their progress. Cutting-edge models can now manage up to 1 million tokens—equivalent to a full-length college textbook—allowing users to control contextual relevance like never before.
For instance, utilizing Anthropic's Claude, I assisted a physician in navigating a complex 700-page guidance document, achieving an 85% accuracy rate on related entrance exams. Additionally, technologies that retrieve information based on concepts rather than keywords are further enriching the knowledge base.
Emerging embedding models, such as titan-v2 and cohere-embed, enable retrieval of related text by converting diverse sources into vectors derived from extensive datasets. Innovations like vector query integration in database systems and specialized vector databases like Turbopuffer are enabling scalability to massive document collections with minimal performance loss.
Despite these advancements, scaling solutions remains challenging, requiring collaboration across various disciplines to optimize security, scalability, latency, cost efficiency, and response quality in LLM applications.
Innovating with Gen 2.0 and Agent Systems
While recent improvements enhance model performance and application viability, we are on the brink of a new evolution: integrating multiple generative AI functionalities.
The initial phase involves creating manual action chains—like the BrainBox.ai ARIA system, which interprets images of equipment malfunctions, accesses relevant knowledge bases, and queries IoT data feeds to suggest solutions. However, these systems face limitations in their logic, either needing hard-coded definitions from developers or restricted to simple decision-making pathways.
The subsequent phase, Gen AI 2.0, envisions agile agent-based systems utilizing multi-modal models, driven by a reasoning engine (typically an LLM). These agents will deconstruct problems into manageable steps and select appropriate AI-driven tools for execution, adapting their approach based on outcomes at each stage.
This modular approach enhances flexibility, enabling systems to tackle complex tasks. For example, Cognition Labs' Devin.ai could automate end-to-end programming tasks, reducing extensive human intervention and completing processes swiftly, while Amazon’s Q for Developers facilitates automatic Java upgrades.
In healthcare, a medical agent system could synthesize EHR data, imaging, genetic information, and clinical literature, yielding comprehensive treatment recommendations. Additionally, multiple specialized agents could collaborate to generate detailed patient profiles and autonomously execute multi-step knowledge processes, reducing the need for human oversight.
Nonetheless, these advanced systems can incur significant costs due to extensive LLM API calls that transmit large token volumes. Therefore, parallel advancements in LLM optimization—spanning hardware (e.g., NVIDIA Blackwell), frameworks (Mojo), cloud (AWS Spot Instances), and model configurations (parameter size, quantization)—are essential to manage expenses effectively.
Conclusion
As organizations evolve in their deployment of LLMs, the focus will shift to achieving high-quality outputs quickly and efficiently. Given the rapid pace of change, partnering with a team experienced in optimizing generative AI solutions is crucial for success.
Ryan Gross is the Senior Director of Data and Applications at Caylent.
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