Learn the basics of Retrieval Augmented Generation in PySpur
Retrieval Augmented Generation (RAG) is a powerful technique that enhances AI models by providing them with relevant information from your own data. Instead of relying solely on what an AI was trained on, RAG lets you ground responses in your specific documents, knowledge bases, and data.
RAG solves several common problems with traditional AI systems:
The RAG process in PySpur follows three simple steps:
Create collections of related documents to reference (PDFs, Word docs, text files, etc.).
PySpur automatically:
Transform document chunks into searchable vector embeddings:
Integrate RAG into your workflows:
For best results with RAG in PySpur:
Learn the basics of Retrieval Augmented Generation in PySpur
Retrieval Augmented Generation (RAG) is a powerful technique that enhances AI models by providing them with relevant information from your own data. Instead of relying solely on what an AI was trained on, RAG lets you ground responses in your specific documents, knowledge bases, and data.
RAG solves several common problems with traditional AI systems:
The RAG process in PySpur follows three simple steps:
Create collections of related documents to reference (PDFs, Word docs, text files, etc.).
PySpur automatically:
Transform document chunks into searchable vector embeddings:
Integrate RAG into your workflows:
For best results with RAG in PySpur: