Real LLM Success Stories: How They Actually Work // Alex Strick van Linschoten // #287
Jan 31, 2025
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Alex Strick van Linschoten, a Machine Learning Engineer at ZenML with a PhD in History, delves into practical applications of large language models (LLMs). He shares insights from his comprehensive database on LLM use cases, emphasizing both common and innovative applications. The discussion covers the technical challenges of deploying LLMs, the significance of engineering practices, and the evolution of support bots using user behavior insights. Alex also calls for community contributions to enhance collective knowledge in this rapidly changing field.
The creation of a comprehensive database for real-world LLM use cases emphasizes the need to consolidate fragmented information for easy access.
The podcast highlights the importance of innovative UX designs in LLM applications to enhance user engagement beyond traditional chat interfaces.
Deep dives
Building a Comprehensive LLM Use Case Database
The discussion highlights the creation of a large database dedicated to real-world use cases of large language models (LLMs), inspired by existing databases from companies like Evidently AI. The process involved gathering various blog posts and documenting conversations from the MLOps community, showcasing how LLMs are utilized across different scales of operations. This initiative reflects the need to consolidate fragmented information into one accessible platform, allowing users to reference multiple use cases efficiently. The use of LLMs themselves assisted in summarizing posts, making the aggregation manageable without incurring prohibitive costs.
The Diversity of LLM Applications
A notable observation is the variety of LLM use cases, primarily categorized into two broad groups: chatbot implementations for customer service and data interaction. While many organizations replicate common practices seen in the industry, a smaller subset of companies is exploring innovative applications that diverge from traditional use cases. This indicates a burgeoning interest in experimentation, especially among technologically progressive companies that are willing to take risks to discover unique uses for LLM technology. The ongoing exploration underscores that the landscape of LLM use is still emerging and not fully established.
Challenges and Innovations in AI Governance
The podcast touches on the complexities surrounding AI governance, particularly within large enterprises that deploy multiple instances of AI technologies. This complexity leads to problems such as redundant workflows and inefficient licensing processes that can create operational burdens. Additionally, the discussion points out the importance of sharing failures along with successes in AI projects to foster an environment of learning and improvement. Recognizing the challenges in governance can ultimately lead to more effective implementation and management of AI systems within large organizations.
The Role of User Experience in AI Integration
User experience (UX) emerges as a critical theme in the development of applications powered by LLMs, with current designs often limiting user interactions to traditional methods like chat interfaces. As companies integrate LLMs more deeply into their products, there is an opportunity to explore alternative UX designs that enhance user engagement and simplify processes. The discussion advocates for innovation in user interfaces, suggesting that a more playful or flexible interaction model can lead to better outcomes for users struggling to navigate complex applications. Ultimately, the integration of LLMs should aim for a user-centric approach, blending familiar navigation with AI capabilities to improve overall user satisfaction.