Episode description

Highlights from this week’s conversation include:

• Current State of LLMs (1:12)
• Historical Analogy to the iPhone (3:32)
• Limitations of Early iPhones (5:02)
• Comparing LLMs to Historical Technologies (6:08)
• Skepticism About LLM Capabilities (9:11)
• Broad Nature of AI Innovations (10:12)
• User Input Challenges (14:32)
• Transcription and Unstructured Data (16:19)
• Single Player vs. Multiplayer Experiences with LLMs (18:50)
• Revenue Insights from ChatGPT (20:27)
• Contextual Use of LLMs in Development (23:43)
• Implications of Human Involvement (26:15)
• The Role of Human Feedback (29:19)
• Customer Data Management and LLMs (31:25)
• Streamlining Data Engineering Processes (34:24)
• Prototyping Content Recommendations (37:42)
• Summarizing Content for LLMs (39:51)
• Challenges with Output Quality (41:18)
• Data Formatting for Marketing Use (43:20)
• Efficient Workflow Integration (46:20)
• Exploring New Prototyping Techniques (50:56)
• Distance Metrics for Improved Relevance (53:00)
• Improving Search Techniques (56:46)
• Utilizing LLMs in Customer Data (59:15)
• Challenges in Customer Data Processing (1:01:10)
• Final thoughts and takeaways (1:02:12)

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.

RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.