15 Advanced RAG Techniques from Pre-Retrieval to Generation

Retrieval augmented generation (RAG) is a rich, rapidly evolving field that’s creating new opportunities for enhancing generative AI systems powered by large language models (LLMs).

In this guide, the Data & AI Research Team (DART) at WillowTree shares 15 advanced RAG techniques for fine-tuning your own system, all of which we trust when optimizing our clients’ applications.

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Why We Wrote This Guide on Advanced RAG Techniques

WillowTree’s LLM experimentation and research, drawn from client consulting engagements and ongoing development efforts, highlights the value advanced RAG techniques offer businesses that want to leverage generative AI. In particular, we see significant potential for advanced RAG techniques to improve:

information density from retrieved documents
information retrieval accuracy
user query response quality

With the techniques in this guide, you can build and fine-tune advanced RAG systems that enhance the performance of your underlying large language models (LLMs). That means:

greater system efficiency
better alignment with user needs
optimal semantic search
increasingly relevant, concise, and accurate response generation by your LLMs

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In this guide, you’ll learn:

How to integrate advanced RAG strategies at each stage — pre-retrieval and data indexing, information retrieval, post-retrieval, and generation
The importance of testing techniques in your RAG system, from optimizing chunks and embeddings to retrieving more relevant information with reranking
Recommendations for further advanced RAG techniques to check out

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