Building LLM-Based Applications with Azure OpenAI with Jay Emery - #657
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Challenges of Incorporating LLMs into Core Business Systems
Businesses are likely to become more skilled at applying vector databases to speed up data processing. However, incorporating LLM responses into core business systems and workflows presents challenges such as inference performance issues, misconceptions about solving problems with more hardware, and the difficulty of achieving efficiency and low latency. The complexity and compute demands behind the scenes are significant, with one GPT prompt response potentially taking 39 hours to process using a single Intel processor. Developing apps or products with LLM integration requires acknowledging the persistence of latency and understanding that there is no quick fix, although customers can find ways to adapt.
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