Six-month results show substantial gains: recall climbed from about 78% to over 95%, latency dropped from roughly 850 ms to 320 ms, with fewer hallucinations and lower cost per query.

RAG enables language models to fetch external information at query time by retrieving relevant documents and injecting them into prompts to ground outputs in retrieved context.

Common RAG use cases include enterprise Q&A over internal docs, customer support copilots, legal and financial review, code assistants, and scientific/technical research.

GraphRAG adds a knowledge-graph layer to support multi-hop reasoning and aggregations, boosting accuracy on complex queries and designed to complement vector RAG rather than replace it.

Core RAG components remain embeddings, vector stores, and retrieval, with a hybrid of dense-semantic and sparse-term signals improving accuracy and relevance.

Agentic RAG introduces control loops like query rewriting, tool usage, and self-correction, but it increases LLM calls and costs, so solid baseline retrieval is essential first.

Real-world RAG challenges include misaligned chunking (512-token windows), long documents splitting mid-sentence, noisy signals from large chunks, and latency contributions from embedding, vector search, and LLMs.

Traditional RAG issues center on fixed chunking breaking sentences, drowning signals, and overall latency, underscoring the need for a principled retrieval layer.

Producing reliable RAG deployments requires addressing chunking, latency breakdowns, and ensuring robust retrieval as a foundation.

We’re rolling out a production metrics framework for RAG that instruments retrieval latency, recall@k on a golden set, query expansion counts, and reranker latency to enable ongoing monitoring and optimization.

The shift is a mental one: treat retrieval as infrastructure, adopt versioned chunking strategies, enforce a strict evaluation CI, and focus on answer correctness for users rather than embedding models alone.

In 2026, RAG aims to bridge gaps where frontier models lack access to private or internal data and emphasizes keeping data in user-controlled systems with source-backed outputs.