Exhibition
What is RAG?
RAG (retrieval-augmented generation) fetches relevant chunks from your knowledge base before the model answers—reducing guesses on company or legal text.
How RAG works
- Ingest documents into chunks and embeddings
- Retrieve top matches for each question
- Inject passages into the prompt
- Model answers citing retrieved text
RAG and agents
Document agents combine RAG with actions: summarize, extract tables, or file tickets when a clause matches a rule.
PDF agent example
Use the interactive demo on MEAGENT or open a product guide—start with a small, low-risk task.
PDF agent example