# RAG Evaluations

1 min read

This section collects all research and evaluation work related to **Retrieval-Augmented Generation (RAG)** at Unique. RAG is the core retrieval paradigm underlying our AI products, and improving it is a continuous area of investment.

The work here spans foundational benchmarking of our standard retrieval setup, explorations of graph-based retrieval approaches (GraphRAG, LightRAG), and empirical comparisons of model and system performance within RAG pipelines. Each page represents a concrete research effort — with methodology, findings, and strategic conclusions.

## What is RAG?

Retrieval-Augmented Generation is a technique that enhances large language model (LLM) responses by first retrieving relevant documents from a knowledge base, then providing those documents as context for answer generation. This grounds the model in real, up-to-date data rather than relying solely on its training knowledge.

At Unique, our RAG pipeline combines **vector search** and **full-text search** (combined/hybrid search) with optional **reranking** to surface the most relevant chunks from customer document sets before passing them to the LLM.

## Research areas

- **Retrieval quality assessment** — Measuring recall and ranking metrics (NDCG) across retrieval strategies.
- **Graph-based retrieval** — Exploring knowledge graph augmentation via GraphRAG and LightRAG to handle complex aggregation queries.
- **Agentic retrieval** — How agentic search (UniqueAI-Chat) compares to and extends standard RAG for multi-step reasoning tasks.