Como treinei uma IA de suporte com histórico real de atendimento: da conversa bruta ao RAG em produção

1. 1Collect 8,400 raw conversations from customer support history
2. 2Preprocess conversations to extract relevant information
3. 3Transform conversations into 2,200 knowledge pairs without manual annotation
4. 4Build a vector database to store the knowledge pairs
5. 5Fine-tune a Large Language Model (LLM) using the vector database
6. 6Deploy the trained LLM in production using RAG (Retrieval-Augmented Generation)

This pipeline benefits data scientists, AI engineers, and product managers who want to develop and deploy AI-powered support systems, as it provides a comprehensive approach to training a support AI with real-world data

💡 Using a pipeline to extract knowledge from raw conversations and transform it into a vector base can enable the development of effective AI-powered support systems without requiring manual annotation

Train a support AI with real attendance history using a pipeline that extracts knowledge and transforms it into a vector base #AI #LLM #RAG