Retrieval augmented generation (RAG) is a technique that helps improve large language model outputs by pulling from external knowledge bases. According to Jerry Liu, co-founder and CEO of LlamaIndex, basic RAG systems have limitations like primitive interfaces, poor quality understanding, and lack of memory. This can make it challenging to scale LLM apps due to accuracy issues and technical complexities.
LlamaIndex aims to address these challenges by offering a platform that helps developers build next-generation LLM-powered apps easily. The platform includes data extraction to convert unstructured data into programmatically accessible formats, RAG for answering queries, and autonomous agents. Liu emphasized the importance of synchronizing different types of data within an enterprise to ensure data quality and relevant context for queries.
LlamaCloud, available by waitlist, features advanced ETL capabilities to synchronize data over time for freshness. The company’s LllamaParse, an advanced document parser, helps reduce LLM hallucinations and has been widely used in various industries. Liu highlighted the importance of multi-agent systems for better query understanding and tool use, as well as the benefits of parallelization and specialization in solving complex tasks.
By incorporating agentic reasoning and multiple agents, LlamaIndex aims to optimize cost, reduce latency, and improve overall task performance. Liu emphasized the collaborative nature of multi-agents in solving higher-level tasks through communication and specialization. The platform has been utilized in financial analyst assistance, internet search, analytics dashboards, and internal LLM application development across industries like technology, consulting, finance, and healthcare.