rag-pipeline

Retrieval-augmented generation grounds LLM answers in retrieved documents to reduce hallucinations and increase citation reliability for knowledge-intensive tasks. It applies provenance-aware prompts to ensure answers are backed by source data.

An end-to-end RAG pipeline implements chunking, embedding selection, vector databases, retrieval, reranking, and generation. Configuring these components ensures grounded answers with provenance for your document corpus or private data.

Hybrid search and reranking improves retrieval-augmented generation by combining keyword and vector similarity to increase document relevance. This approach ensures provenance-aware prompts receive the most accurate context for citation-backed answers.

Yes, a RAG pipeline supports internal knowledge bases and private data requiring citation-backed answers. It applies chunking, embedding selection, and vector storage to ground LLM responses in your private documentation.

Yes, a vector database is a core component of retrieval-augmented generation pipelines. It stores document embeddings and enables the vector similarity search required for retrieving relevant context before generation.

Your LLM lacks citation reliability because it is not grounded in retrieved documents. Implementing a RAG pipeline with provenance-aware prompts and hybrid search grounds answers in source documentation to provide reliable citations.