Model Solutioning Insights
The discussion highlights the importance of leveraging pretrained models and the effectiveness of few-shot generation for various use cases. Fine tuning comes into play when more specific solutions are needed, especially when ample labeled examples are available. There's a vast potential in exploring the capabilities of APIs and chaining models together, which remains largely underutilized in the current landscape.In this clip
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Practical AI
Applied NLP solutions & AI education
Related Questions
Can you explain more about how AI models are trained as discussed in the episode Treating Prompt Engineering More Like Code // Maxime Beauchemin // MLOps Podcast #167 and the clip Fine-tuning Models?
Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Collaboration & evaluation for LLM apps and the clip Fine Tuning Insights?
Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Collaboration & evaluation for LLM apps and the clip Fine Tuning Insights?