Retrieval Augmented Generation - A Simple Introduction

How to make a ChatGPT or a Bard for your own data❓
The answer is in creating an organisation “knowledge brain” and use Retrieval Augmented Generation.

LLMs have rapidly advanced to surpass many benchmarks. However, their real-world utility remains constrained by knowledge limitations that cause hallucinations or unsupported responses. This is where Retrieval Augmented Generation (RAG) comes in - it can supercharge LLMs like llama2, mistral, gpt4 by connecting them to vast external knowledge. 📚

In this short introduction to Retrieval Augmented Generation, you'll find answers to -

• What is Retrieval Augmented Generation?
• How does RAG help?
• What are some popular RAG use cases?
• What does the RAG Architecture look like?
• What are Embeddings?
• What are Vector Stores?
• What are the best retrieval strategies?
• How to Evaluate RAG outputs?
• RAG vs Finetuning - What is better?
• How does the evolving LLMOps Stack look like?
• What is Multimodal RAG?
• What is Naive, Advanced and Modular RAG?

You'll also see some examples of RAG components with the usage of LangChain, LlamaIndex, HuggingFace, OpenAI and more.

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