15 advanced RAG techniques from pre-retrieval to generation
In this guide, the Data & AI Research Team (DART) at TELUS Digital shares 15 advanced retrieval augmented generation (RAG) techniques for fine-tuning your own system, all of which we trust when optimizing our clients’ applications.
Retrieval augmented generation is a rich, rapidly evolving field that’s creating new opportunities for enhancing generative AI systems powered by large language models (LLMs). Correctly implemented, these techniques drive greater cost efficiencies for businesses and a better customer experience.
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
Build and fine-tune advanced RAG systems
TELUS Digital'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 LLMs. That means:
- Greater system efficiency
- Better alignment with user needs
- Optimal semantic search
- Increasingly relevant, concise and accurate response generation by your LLMs
