What Is Retrieval Augmented Generation (RAG) and How Does It Reduce Hallucinations?

Updated

Retrieval augmented generation (RAG) is a method that reduces hallucination by retrieving relevant passages from your own knowledge base and passing them to a large language model (LLM) as evidence, so the model answers from supplied material instead of relying only on its training memory. It fits any case where answers must be grounded in a specific, changing, or private document set — support docs, legal corpora, research papers, internal wikis. It is not the right tool when the task is pure creative writing or when the knowledge is already fully captured in the model's weights and never changes.

The core loop

RAG is a pipeline, not a single model. Four stages repeat at query time:

  1. Ingest and chunk — documents are split into passages small enough to retrieve precisely but large enough to carry meaning.
  2. Embed and index — each chunk is converted into a vector by an embedding model and stored in a vector database.
  3. Retrieve — the user's question is embedded the same way, and the database returns the closest chunks, often filtered by metadata.
  4. Generate — the retrieved chunks are inserted into the prompt, and the LLM writes an answer grounded in them, ideally with citations.

The generation step is where hallucination is suppressed: the model's job shifts from "recall the answer" to "read these passages and answer."

Why this reduces hallucination

An LLM asked a factual question with no context must produce an answer from statistical memory. When the fact is rare, recent, or private, the model has no reliable signal, so it produces fluent text that may be wrong. RAG changes the task:

  • Evidence is in the prompt. The model conditions on retrieved text rather than guessing.
  • Citations make errors visible. When the system cites its sources, a user can check whether the answer actually follows from the passage.
  • Scope can be narrowed. Metadata filtering restricts retrieval to a subset — one product line, one jurisdiction, one date range — so the model is not blending unrelated material.

Agentset's platform describes this directly: it "automatically cites the sources of your answers, allowing your users to inspect it," and supports metadata filtering "to base answers on a subset of the data." Its stated positioning is "reliable answers, fewer surprises," with accuracy benchmarks on MultiHopQA and FinanceBench before any customization.

RAG vs. fine-tuning vs. plain semantic search

These solve different problems. Choosing wrong is a common source of disappointment.

Approach What it changes Best when Weakness
RAG What the model sees at answer time Knowledge is large, private, or frequently updated Depends on retrieval quality
Fine-tuning The model's weights You need a consistent style, format, or narrow skill Expensive to update; still can hallucinate facts
Semantic search Returns passages, no generation Users want to read sources themselves No synthesized answer

A practical rule: if the knowledge changes or must be cited, use RAG. If the behavior (tone, output schema) is the problem, fine-tuning is the lever. They combine well — fine-tune for form, retrieve for facts.

Where RAG pipelines fail

Most "RAG doesn't work" reports trace to one of these:

  • Chunking granularity. Chunks too small lose context; too large dilute the embedding and waste context window. Tables and figures need their own handling.
  • Retrieval recall. If the right passage is not in the top results, no amount of prompting recovers it. Recall is the ceiling on answer quality.
  • Context length. Stuffing too many chunks crowds the prompt and can bury the relevant one. Retrieve fewer, better passages.
  • Missing citations. Without source attribution, users cannot verify, and errors go unnoticed.
  • Ignoring non-text content. Real knowledge bases contain images, graphs, and tables. A pipeline that only reads text will silently miss answers.

Agentset addresses the last point explicitly: it "natively works with images, graphs, and tables just like text," and its ingestion supports 22+ file formats including PDF, DOCX, XLSX, PPTX, CSV, HTML, and email formats (.EML, .MSG).

What to check before adopting a RAG platform

  • Model and database choice. A vendor-locked stack limits you later. Agentset is model-agnostic — you select your own vector database, embedding model, and LLM — and lists integrations across OpenAI, Anthropic, Google AI, Azure, Cohere, Mistral, DeepSeek, Qwen, Pinecone, and Qdrant.
  • Developer surface. Look for SDKs in the languages you use. Agentset ships JavaScript and Python SDKs, an MCP server for connecting knowledge bases to external applications, and AI SDK integration.
  • Feedback loop. Can users flag bad answers? Agentset supports preview links with a customizable chat interface for capturing external feedback.
  • Accuracy evidence. Ask for benchmarks on tasks resembling yours. Agentset cites MultiHopQA and FinanceBench results, and a customer reference from Gyldendal Rettsdata describes a legal corpus of 3 million pages now powering a product line.

Pricing and login requirements are not specified in the available material, so confirm those directly on the vendor's pricing page before committing.