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.
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insights INSIGHT
Inspiration for LLM Database
Alex Strick van Linschoten was inspired by Evidently AI's databases of ML/AI use cases.
He created a similar database for LLMs, consolidating information from various sources like blogs and podcasts.
insights INSIGHT
Varied LLM Use Cases
LLM use cases are varied, unlike traditional ML, making it challenging to identify consistent value propositions.
Many companies are simply replicating common chatbot implementations rather than exploring innovative applications.
question_answer ANECDOTE
Weights and Biases' Chatbot
Weights and Biases shared their experience building an internal support chatbot, including their evaluation mistakes and associated costs.
This transparency about failures is valuable for the community, but less common among larger corporations.
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Timestamps:[00:00] Alex's preferred tea[00:15] Takeaways[00:55] LLM Database Creation Insights[03:26] Hidden Gems and LLMs[07:04] Chatbot Governance Challenges[15:16] AI Agents and IPOs[19:51] AI Interface Evolution[23:56] LLMs as Product Guides[26:57] RAG with User Context[30:29] User Experience Friction Points[36:20] ROI and Engineering Insights[41:09] Agent Debugging and Flows[45:28] Data Viz Ideas LLM[47:41] Wrap up
MLOps.community