Getting Started with RAG
Generative AI is transforming business, but Large Language Models (LLMs) have a flaw: they don't know your private data. This is where Retrieval-Augmented Generation (RAG) comes in.
What is RAG?
RAG is a technique that enhances LLM accuracy by fetching relevant data from your internal knowledge base before generating a response.
How it Works
- Ingestion: Documents are split into chunks and converted into vector embeddings.
- Retrieval: When a user asks a question, the system finds the most relevant chunks.
- Generation: The LLM uses these chunks as context to answer the question accurately.
Why it Matters
- Accuracy: Reduces hallucinations by grounding answers in facts.
- Security: Keeps your data private while leveraging powerful models.
- Freshness: No need to retrain models when data changes.
Ready to build your first RAG application? Get in touch.
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