Fine Tuning Models
Fine tuning a vector database involves enriching real-world conversations with synthetic data to enhance accuracy in specific domains. Evaluating these models requires a robust approach beyond internal testing, as bias can skew results. Utilizing automated systems and external testers helps ensure a more objective assessment, capturing the nuances of customer interactions effectively.In this clip
From this podcast
Eye on AI
Is Voice AI the Future of Customer Service? | Itamar Arel
Related Questions
How are large language models (LLMs) fine-tuned post-training in the context of the episode Is Voice AI the Future of Customer Service? | Itamar Arel and the clip Fine-Tuning Framework?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode Vector Databases and the Power of RAG and the clip Evolution of AI, as well as in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Have you seen a way to unit test large language models (LLMs) that are super helpful, as discussed in the episode How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269 and the clip Metric-Driven Experimentation?