RAG
Retrieval-Augmented Generation — an AI architecture that grounds LLM responses in retrieved documents rather than relying solely on parametric training knowledge.
What is RAG?
RAG (Retrieval-Augmented Generation) is an AI architecture that combines a retrieval system with a large language model (LLM) to produce factually grounded responses. When a query arrives, the retrieval layer (typically a vector store or semantic search index) retrieves relevant documents from a curated knowledge base. These retrieved chunks are injected into the LLM's context window as grounding material before the model generates a response.
RAG solves the core problem of LLM hallucination for knowledge-intensive tasks: without retrieval, a model answers from its parametric training knowledge, which may be outdated, incomplete, or inaccurate for domain-specific questions. With retrieval, answers are grounded in current, authoritative source documents — and citations can be surfaced so users can verify responses.
A production RAG system comprises several interdependent components: a document ingestion and chunking pipeline, an embedding model that converts text to dense vector representations, a vector store for approximate nearest-neighbour retrieval, a reranker that scores retrieved chunks for relevance, and an LLM that generates responses conditioned on retrieved context. Evaluation harnesses and drift monitoring are required to maintain quality over time as documents and user queries evolve.
AiRAT builds production RAG systems with evaluation harnesses, PII boundaries, human review checkpoints, and observability designed from the first sprint. One FinTech client's legal team now queries 2.5 million documents in 40 seconds — work that previously required three days of analyst time.
Examples in production
Legal document retrieval
A FinTech compliance team queries a 2.5M-document repository in natural language. RAG retrieves the five most relevant policy clauses, grounds the LLM's response in those specific documents, and surfaces citations so analysts can verify each claim.
SOC threat intelligence
A SOC analyst asks 'what tactics does Lazarus Group typically use for initial access?' — RAG retrieves current threat intel reports, IOC databases, and previous case notes, grounding the LLM's answer in live intelligence rather than training-time knowledge.
Product support automation
A customer support RAG system retrieves relevant FAQ chunks and release notes for each query, grounding responses in current documentation rather than generating plausible-but-incorrect answers from model parameters.
Questions about RAG
What is the difference between RAG and fine-tuning?
Fine-tuning adapts a model's weights on domain-specific data — improving style, tone, and domain vocabulary, but not guaranteeing up-to-date factual accuracy. RAG retrieves current documents at inference time, providing factual grounding without modifying model weights. Most production AI systems use both: fine-tuning for domain adaptation and RAG for knowledge currency and citation support.
What causes RAG to fail in production?
The most common production failures are: poor chunking strategy (chunks too large or too small lose context or miss relevant passages); embedding model mismatch (embedding model trained on different domain than retrieval corpus); retrieval recall failure (relevant documents not retrieved due to vocabulary gaps or poor query expansion); and reranker drift (reranker scores degrade as corpus grows and is not re-evaluated). AiRAT builds evaluation harnesses that catch each failure class before it reaches users.
How do you evaluate RAG quality in production?
A robust RAG evaluation stack covers: retrieval recall and precision (are the right chunks being retrieved?), faithfulness (does the generated answer stay grounded in retrieved documents?), answer relevance (does the answer address the query?), and context utilisation (is retrieved context being used, or is the model ignoring it?). Continuous monitoring of these metrics alongside business proxies (task completion rate, escalation rate) is required after go-live.
Does AiRAT build RAG systems?
Yes — RAG pipeline design, evaluation harness implementation, and production monitoring are core to our AI & LLM Systems service. We have delivered governed RAG systems for FinTech, legal, and SOC environments. Book a strategy call to scope your RAG programme.
Running a system that uses RAG?
We build production-grade ai & llm systems systems for regulated enterprises.