RAG (Retrieval Augmented Generation)

What Is RAG (Retrieval Augmented Generation)?

Retrieval Augmented Generation (RAG) is an AI architecture that combines information retrieval with text generation to produce accurate, grounded responses. Instead of relying solely on a large language model's (LLM) internal knowledge, which can be outdated or incomplete, RAG retrieves relevant documents from an external knowledge source and passes that context to the language model during generation. This retrieve-then-generate approach is the foundation of modern AI customer service platforms because it reduces AI hallucination and ensures answers are grounded in verified, current information.

How RAG Works

The RAG pipeline operates in two primary stages: retrieval and generation.

Retrieval stage: When a customer submits a query, the system converts it into a dense vector embedding, a numerical representation that captures semantic meaning. This embedding is compared against a vector index of pre-processed documents (help articles, product docs, policy pages, past tickets) using similarity search algorithms like cosine similarity or approximate nearest neighbor (ANN) search. The most relevant document chunks are retrieved and ranked. Advanced implementations augment vector search with knowledge graph lookups and keyword-based retrieval (hybrid search) to capture both semantic meaning and exact-match terms like product codes or error IDs.

Generation stage: The retrieved document chunks are injected into the LLM's prompt as context. The language model generates a response that synthesizes the retrieved information into a coherent, natural-language answer. Because the model is conditioned on real source material rather than its parametric memory alone, the response is factually grounded and traceable to specific documents.

Production-grade RAG systems add intermediate steps: query rewriting to improve retrieval quality, reranking to prioritize the most relevant chunks, and RAG Fusion techniques that merge results from multiple retrieval strategies. These refinements reduce noise and improve precision.

Why RAG Matters

Large language models are powerful generators, but they have a critical limitation: they can produce information that sounds correct but is factually wrong. This is hallucination, and in customer service it means giving a customer an incorrect billing policy, a wrong troubleshooting step, or a non-existent feature. RAG addresses this by anchoring every response in retrieved evidence.

The foundational RAG paper by Lewis et al. (2020), published at NeurIPS, demonstrated that combining a pretrained language model with a neural retriever over external documents produced more specific, diverse, and factual outputs than parametric-only models. This architecture has since become the standard for knowledge-intensive AI applications, as documented in the original RAG research on arXiv.

For customer service, RAG's impact is direct. The AI Agent can cite its sources, support teams can verify accuracy, and customers receive answers based on the company's actual knowledge, not the model's best guess. RAG also makes it straightforward to keep AI current: update the document index, and the AI immediately reflects new information without retraining.

Use Cases and Applications

RAG is the technical backbone of accurate AI support across industries:

• Customer support: Retrieving help articles, product documentation, and account data to generate precise answers to customer questions
• Internal knowledge management: Enabling employees to query across Confluence, Notion, Google Drive, and other repositories using natural language
• Compliance and regulated industries: Grounding every AI response in approved policy documents and maintaining citation trails for audit
• Technical troubleshooting: Pulling error documentation, API references, and past resolution patterns to guide SaaS support interactions
• Multilingual support: Retrieving source documents in one language and generating responses in the customer's preferred language

The Maven Advantage

Maven AGI's platform is built on a sophisticated RAG architecture. Agent Maven uses hybrid search combining dense vector embeddings with structured knowledge graph lookups across 100+ connected data sources. Multi-stage reranking and context fusion ensure the most relevant information reaches the generation layer, producing accurate, complete responses.

Enumerate, a PropTech company, achieved a 91% resolution rate with Maven AGI. This level of accuracy is only possible because Maven's RAG pipeline retrieves verified information from the company's own knowledge base and product systems, not from the LLM's parametric memory, eliminating the hallucination risk that undermines trust in AI support.

Maven AGI, backed by $78M in funding, treats RAG as a foundational layer. Every response Agent Maven generates is grounded in retrieved evidence, with source citations for transparency. This is why Maven customers like K1x see 80% resolution (10x over prior AI) and Papaya Pay achieves 90% autonomous resolution. Learn more in the IBM Research on Retrieval Augmented Generation, or explore how hybrid retrieval is advancing in recent research on RAG with knowledge graphs.

Frequently Asked Questions

How does RAG reduce AI hallucination?

RAG reduces hallucination by conditioning the language model's output on retrieved source documents rather than relying on internal parameters alone. The model draws from specific, verified text passages. If the retrieved context does not contain the answer, well-designed RAG systems acknowledge the gap rather than fabricating a response.

What are vector embeddings and why do they matter for RAG?

Vector embeddings are numerical representations of text that capture semantic meaning. The word "refund" and the phrase "get my money back" would have similar embeddings even though they share no words. In RAG, embeddings enable semantic search: the system finds conceptually relevant documents, not just keyword matches. This is what makes RAG far more effective than traditional search for conversational AI.

Does RAG require retraining the language model?

No. One of RAG's key advantages is that the language model does not need retraining when information changes. Teams update the document index, and the retrieval layer immediately surfaces current information during generation. This makes RAG ideal for fast-moving environments like SaaS support where documentation changes frequently.

What is hybrid search in a RAG system?

Hybrid search combines vector-based semantic search with keyword-based (BM25 or similar) retrieval. Vector search excels at understanding meaning and intent, while keyword search is better for exact matches like product codes and error IDs. Combining both ensures the RAG pipeline retrieves relevant results whether the query is conversational or specific. Maven AGI uses this hybrid approach across its enterprise AI support platform.

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