LLM Product Challenges

Building products with large language models presents unique challenges, particularly around context windows. As the length of text input and output increases, the computational demands grow exponentially, leading to potential slowdowns and hallucinations in model outputs. Solutions exist, such as advanced models with larger context windows, but they come with trade-offs that require careful consideration.

In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
688: Six Reasons Why Building LLM Products Is Tricky — with Jon Krohn (@JonKrohnLearns)
Related Questions
- What challenges are faced in training large language models (LLMs) as discussed in the episode Jennifer Prendki Interview - Agile Machine Learning - TWiML Talk #46 and the clip Monitoring NLP Models?

LLM Product Challenges

In this clip

From this podcast

Super Data Science: ML & AI Podcast with Jon Krohn

688: Six Reasons Why Building LLM Products Is Tricky — with Jon Krohn (@JonKrohnLearns)

Related Questions

What challenges are faced in training large language models (LLMs) as discussed in the episode Jennifer Prendki Interview - Agile Machine Learning - TWiML Talk #46 and the clip Monitoring NLP Models?