AI Search and RAG - Technical Research and Growth Vectors

RAG Systems Are Becoming More Capable, Driven by the Need for Accurate, Domain-Specific and Cost-Efficient AI Applications

Retrieval Augmented Generation has become increasingly important, driven by the growing experimentation and deployment of AI applications in the Enterprise, where factual reliability and accuracy are key requirements. RAG’s ability to mitigate LLM hallucinations by providing sources is the best tool we have currently to minimize the possibility of incorrect information being generated by AI applications.

But RAG systems also have challenges:

• Irrelevant document retrieval may result in unhelpful response generation or even deteriorate the performance of LLMs

• Observability is more complex and needs to be fine-grained, as not only the system as a whole, but also its individual parts (i.e. retriever, generator) need to be evaluated

• RAG orchestration comes with its own trade-offs, such as whether to introduce external information at every conversational turn or only at certain points

  

To address these challenges and others, many technical papers have been published on RAG systems in the past few months, focusing on improvements in areas such as performance and speed, orchestration, and observability tools.

This research is also important because RAG is an early type of AI agent, and this evolution in technical capabilities, complexity and variety of systems is a step towards and will also apply to more advanced types of AI agents. As RAG systems become more advanced, they demonstrate the potential to build applications that can utilize a greater variety of software tools and make increasingly complex decisions, thereby replacing more repetitive tasks and freeing up time and capital.

RAG systems are growing in multiple ways: