Everything you need to know about Retrieval Augmented Generation in one human-friendly guide.

Generative AI models struggle when you ask them about facts not covered in their training data. Retrieval Augmented Generation―or RAG―enhances an LLM's available data by adding context from an external knowledge base, so it can answer accurately about proprietary content, recent information, and even live conversations. RAG is powerful, and with A Simple Guide to Retrieval Augmented Generation, it's also easy to understand and implement!

In A Simple Guide to Retrieval Augmented Generation you'll learn:

The components of a RAG system
How to create a RAG knowledge base
The indexing and generation pipeline
Evaluating a RAG system
Advanced RAG strategies
RAG tools, technologies, and frameworks

A Simple Guide to Retrieval Augmented Generation shows you how to enhance an LLM with relevant data, increasing factual accuracy and reducing hallucination. Your customer service chatbots can quote your company's policies, your teaching tools can draw directly from your syllabus, and your work assistants can access your organization's minutes, notes, and files.

Acerca del autor

Abhinav Kimothi is an entrepreneur and Vice President of Artificial Intelligence at Yarnit. He has spent over 15 years consulting and leadership roles in data science, machine learning and AI.

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