The InfoQ Podcast
Apoorva Joshi on LLM Application Evaluation and Performance Improvements
Jan 30, 2025
In this conversation, Apoorva Joshi, a Senior AI Developer Advocate at MongoDB with a rich background in cybersecurity and machine learning, delves into the intricacies of evaluating LLM applications. He discusses strategies for optimizing performance through observability and monitoring, and the evolution of LLMs from text generation to complex multimedia tasks. Joshi also highlights the importance of tailored evaluations for specific industries and makes a case for democratizing these models for broader accessibility.
30:48
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Quick takeaways
- Evaluating LLM applications necessitates nuanced metrics like coherence and relevance as opposed to traditional performance indicators.
- The integration of sophisticated data retrieval techniques is vital for enhancing the contextual relevance of information delivered by LLMs.
Deep dives
The Role and Evolution of Large Language Models
Large Language Models (LLMs) have become foundational in generative AI applications, contributing significantly to various business and technology use cases. They are utilized not only for direct user-facing applications but also for enhancing software development processes, such as automating code generation and improving system upgrades. For instance, AI agents can now autonomously handle software updates and generate Jira tickets, drastically reducing the time required for patching from days to mere hours. The current trajectory of LLMs is shifting from text generation to generating diverse content types like images, audio, and video, indicating a growing adaptability in addressing a wider array of applications.
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