Vector Databases and Retrieval-Augmented Generation for Accurate AI-Driven Project Estimation

Large Language Models (LLMs) can analyse unstructured enterprise documents but lack organizational memory, resulting in inconsistent and unreliable software project estimations. Retrieval-Augmented Generation (RAG) addresses this limitation by integrating vector databases that store semantic embeddings of historical project artifacts. Using similarity-based retrieval rather than keyword matching, RAG enables LLMs to ground estimations in relevant past projects despite variations in terminology or technology. This evidence-driven approach improves estimation accuracy, consistency, and explainability, providing a scalable foundation for AI-assisted software project estimation in enterprise environments.