Introduction
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Retrieval-Augmented Generation (RAG) is at the forefront of AI applications today.
Though still a burgeoning field, numerous companies have integrated RAG into their software and workflows, continually pushing for improvements.
Although AI inherently involves complexities, the collaborative efforts of individuals, software communities, and organizations have led to tools, APIs, and resources that significantly streamline the process of building AI applications like RAG systems. Abstractions and sensible defaults make these tools more accessible while offering enough flexibility to craft unique solutions.
By the end of this lesson, you will have learned to:
- Design and implement a simple RAG pipeline using LangChain components.
- Integrate LLMs (e.g., OpenAI’s GPT models) with retrieved context.
- Handle data preparation and chunking for effective retrieval.