How LlamaIndex is ushering in the future of RAG for enterprises

by | Jul 10, 2024 | Technology

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Retrieval augmented generation (RAG) is an important technique that pulls from external knowledge bases to help improve the quality of large language model (LLM) outputs. It also provides transparency into model sources that humans can cross-check.

However, according to Jerry Liu, co-founder and CEO of LlamaIndex, basic RAG systems can have primitive interfaces and poor quality understanding and planning, lack function calling or tool use and are stateless (with no memory). Data silos only exacerbate this problem. Liu spoke during VB Transform in San Francisco yesterday.

This can make it difficult to productionize LLM apps at scale, due to accuracy issues, difficulties with scaling and too many required parameters (requiring deep-tech expertise).

This means that there are many questions RAG simply can’t answer.

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“RAG was really just the beginning,” Liu said onstage this week at VB Transform. Many core concepts of naive RAG are “kind of dumb” and make “very suboptimal decisions.”

LlamaIndex aims to transcend these challenges by offering a platform that helps developers quickly and simply build next-generation LLM-powered apps. The framework offers data extraction that turns unstructured and semi-structured data into uniform, programmatically accessible formats; RAG that answers queries across internal data through question-answer systems and chatbots; and autonomous agents, Liu explained.

Synchronizing data so it’s always fresh

It is critical to tie together all the different types of data within an enterprise, whether unstructured or structured, Liu noted. Multi-agent systems can then “tap into the wealth of heterogeneous data” that companies contain. 

“Any LLM application is only as good as your data,” said Liu. “If you don’t have good data quality, you’re not going to have good results.”

LlamaCloud — now available by waitlist — features advanced extract, transform load (ETL) capabilities. This allows developers to “synchronize data over time so it’s always fresh,” Liu explained. “When you ask a question, you’re guaranteed to have the relevant context, no matter how complex or high level that question is …

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We want to hear from you! Take our quick AI survey and share your insights on the current state of AI, how you’re implementing it, and what you expect to see in the future. Learn More

Retrieval augmented generation (RAG) is an important technique that pulls from external knowledge bases to help improve the quality of large language model (LLM) outputs. It also provides transparency into model sources that humans can cross-check.

However, according to Jerry Liu, co-founder and CEO of LlamaIndex, basic RAG systems can have primitive interfaces and poor quality understanding and planning, lack function calling or tool use and are stateless (with no memory). Data silos only exacerbate this problem. Liu spoke during VB Transform in San Francisco yesterday.

This can make it difficult to productionize LLM apps at scale, due to accuracy issues, difficulties with scaling and too many required parameters (requiring deep-tech expertise).

This means that there are many questions RAG simply can’t answer.

Register to access VB Transform On-Demand

In-person passes for VB Transform 2024 are now sold out! Don’t miss out—register now for exclusive on-demand access available after the conference. Learn More

“RAG was really just the beginning,” Liu said onstage this week at VB Transform. Many core concepts of naive RAG are “kind of dumb” and make “very suboptimal decisions.”

LlamaIndex aims to transcend these challenges by offering a platform that helps developers quickly and simply build next-generation LLM-powered apps. The framework offers data extraction that turns unstructured and semi-structured data into uniform, programmatically accessible formats; RAG that answers queries across internal data through question-answer systems and chatbots; and autonomous agents, Liu explained.

Synchronizing data so it’s always fresh

It is critical to tie together all the different types of data within an enterprise, whether unstructured or structured, Liu noted. Multi-agent systems can then “tap into the wealth of heterogeneous data” that companies contain. 

“Any LLM application is only as good as your data,” said Liu. “If you don’t have good data quality, you’re not going to have good results.”

LlamaCloud — now available by waitlist — features advanced extract, transform load (ETL) capabilities. This allows developers to “synchronize data over time so it’s always fresh,” Liu explained. “When you ask a question, you’re guaranteed to have the relevant context, no matter how complex or high level that question is …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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