Strategies For Building A Product Using LLMs At DataChat
Mar 3, 2024
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Jignesh Patel discusses the challenges of building a product using Large Language Models, the business and technical difficulties, and strategies for gaining visibility into the inner workings of LLMs while maintaining control and privacy of data. The episode explores the trade-offs in prompt engineering for AI model context building, potential applications of LLMs in information distillation, and the importance of balancing AI regulation and openness for innovation.
Building a product on LLMs poses business challenges due to lack of control over the model.
Technical difficulties arise when using LLMs as a core element due to their black box nature.
Strategies for integrating LLMs include ensembling models, automated testing, and cost-effective utilization.
Deep dives
Machine Learning Evolution and Trends
The podcast episode delves into the evolution of machine learning over the years, discussing the speaker's extensive experience in the field. The conversation covers the transition from early days of big data to the current dominance of Generative AI and Large Language Models (LLMs) like GPT-4. The speaker highlights the intersection of data advancements, hardware progress, and algorithmic sophistication in shaping the current machine learning landscape, emphasizing the excitement in exploring cutting-edge techniques.
Early Engagement with Machine Learning
The speaker reflects on their initial foray into machine learning during the human genome revolution in 2000. Starting with a background in building parallel data processing systems, the shift towards analytics in genomics and proteomics marked a broader approach to data applications. This early engagement required a transition to applying machine learning techniques on extensive data sets to extract insights, paving the way for understanding patterns beyond traditional structured data paradigms.
Innovative Data Chat Approaches with LLMs
The discussion shifts to practical applications, highlighting how DataChat leverages LLMs to facilitate interactive data inquiries and insights. The speaker elaborates on research projects aiming to enable non-technical users to derive insights from structured and unstructured data seamlessly. DataChat's focus on transparency and reproducibility underscores the emphasis on making data science more accessible through LLM-driven automation, enabling dynamic data exploration and query-based responses.
Engineering Challenges and Ensembling Models
Addressing the technical and operational challenges of integrating LLMs, the conversation explores strategies for mitigating risks and enhancing model performance within DataChat. The speaker emphasizes the significance of ensembling multiple LLMs to address latency and cost constraints, leveraging diverse configurations to optimize responses. By emphasizing automated testing frameworks and cost-effective methods for utilizing pre-trained models, DataChat navigates the complexities of LLM integration while adapting to evolving capabilities.
Regulation, Ethics, and Open Source Advocacy
The episode touches on the regulatory landscape and ethical considerations surrounding AI technologies, emphasizing the need for balanced oversight without stifling innovation. Drawing attention to the benefits of open-source and open-weight LLM models, the speaker advocates for transparency and collaborative research efforts to advance responsible AI practices. The importance of empowering organizations to uphold ethical standards and accountability in AI applications underscores the call for regulatory measures focused on organizational conduct rather than model limitations.
Summary Large Language Models (LLMs) have rapidly captured the attention of the world with their impressive capabilities. Unfortunately, they are often unpredictable and unreliable. This makes building a product based on their capabilities a unique challenge. Jignesh Patel is building DataChat to bring the capabilities of LLMs to organizational analytics, allowing anyone to have conversations with their business data. In this episode he shares the methods that he is using to build a product on top of this constantly shifting set of technologies. Announcements
Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Jignesh Patel about working with LLMs; understanding how they work and how to build your own
Interview
Introduction
How did you get involved in machine learning?
Can you start by sharing some of the ways that you are working with LLMs currently?
What are the business challenges involved in building a product on top of an LLM model that you don't own or control?
In the current age of business, your data is often your strategic advantage. How do you avoid losing control of, or leaking that data while interfacing with a hosted LLM API?
What are the technical difficulties related to using an LLM as a core element of a product when they are largely a black box?
What are some strategies for gaining visibility into the inner workings or decision making rules for these models?
What are the factors, whether technical or organizational, that might motivate you to build your own LLM for a business or product?
Can you unpack what it means to "build your own" when it comes to an LLM?
In your work at DataChat, how has the progression of sophistication in LLM technology impacted your own product strategy?
What are the most interesting, innovative, or unexpected ways that you have seen LLMs/DataChat used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working with LLMs?
When is an LLM the wrong choice?
What do you have planned for the future of DataChat?
From your perspective, what is the biggest barrier to adoption of machine learning today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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