LlamaIndex has helped me connect an LLM application to internal documents without writing every part of the ingestion and retrieval pipeline myself. I was able to load documents, split them into manageable sections, generate embeddings, index the content, and retrieve relevant information when users submitted questions. What has been most useful is the ability to experiment with the retrieval process. I adjusted chunk sizes, metadata filters, retrievers, and prompts while keeping the rest of the application mostly unchanged. Review collected by and hosted on G2.com.

The main challenge for me was the learning curve. The basic examples are easy to understand, but building something reliable required learning how documents, nodes, indexes, retrievers, query engines, workflows, and response synthesis work together Review collected by and hosted on G2.com.