What Is Retrieval-Augmented Generation (RAG)?

The knowledge base can contain private documents, product records, or current information that the model did not encounter during training. RAG therefore changes the context available for a specific request while leaving the underlying model unchanged. The following sections explain why models need that external context and how retrieval and generation work together.

Why RAG exists: the problem with relying on model memory alone

An LLM's internal knowledge stops at the cutoff for its training data. Events, policies, prices, and product details published after that point remain outside the model's memory. RAG gives the model access to current sources when it answers, so you can update the knowledge base without retraining the model.

An LLM also lacks automatic access to private information. Company documents, customer records, and internal procedures do not become available unless an application supplies them as context. RAG retrieves relevant passages from approved sources and places them in the prompt. Grounding an answer in those passages can reduce hallucinations, though it cannot prevent every factual error.

Retrieval often costs less than repeatedly retraining a model as information changes. You can refresh documents or indexes while leaving the model itself unchanged. Fine-tuning offers another way to adapt a model, but it serves different needs and requires a separate decision about training cost, maintenance, and intended behavior.

How the retrieval step and generation step work together

A RAG pipeline joins retrieval and generation by placing selected source material in the model's prompt before it writes an answer. Four components divide the work. The knowledge base stores source material, and the retriever finds relevant passages. The integration layer combines those passages with the user's query, and the generator produces the response.

The knowledge base prepares documents for search before any query arrives. It splits each document into chunks and converts each chunk into a numerical representation called an embedding. Chunk size affects retrieval quality. Large chunks preserve more context but may mix relevant details with unrelated material, while small chunks offer greater precision but may separate a statement from the context needed to interpret it.

The retriever searches by meaning rather than relying only on matching words. When a user submits a query, the retriever creates an embedding for it and compares that embedding with the stored chunk embeddings. Chunks with nearby representations rank as more semantically similar to the query.

The integration layer then inserts the top-ranked chunks into an augmented prompt alongside the original query and any response instructions. The generator reads that prompt and writes an answer using both its trained language capabilities and the retrieved material. Retrieval quality determines what evidence reaches the generator, while prompt construction determines how clearly the generator can use it.

RAG vs. fine-tuning vs. long-context prompting

RAG fills a knowledge gap by retrieving external information when a request arrives. Fine-tuning changes model behavior by updating its weights, while long-context prompting places the relevant material directly in one request.

A practical model-optimization sequence starts with prompting. You can add RAG when the model lacks domain knowledge, then consider fine-tuning when prompting and retrieval still cannot produce the required behavior.

Production systems can combine these methods. A fine-tuned model can provide consistent behavior while RAG supplies current facts. RAG can also select relevant documents for a long-context model to analyze together.

RAG inside an agent workflow

Agentic RAG lets an AI agent retrieve evidence whenever a task requires it, including after reasoning has begun. Traditional RAG follows a fixed retrieve-once and generate-once sequence, so the model cannot correct an incomplete search. An agentic retrieval loop can revise queries, retrieve across multiple sources, and decide whether the available evidence supports an answer.

A planner first breaks the task into steps, and the agent then calls retrieval or other tools as needed. Memory carries useful findings into later steps. Reflection lets the agent inspect an intermediate result and search again when evidence conflicts or leaves a gap.

For example, an agent reviewing a contract might retrieve the standard cancellation policy first. A clause in the contract could then prompt a second search for an account-specific amendment. Static RAG cannot plan the second query because the need for it appears only after the first document has been read.

Sim's native Knowledge Bases make retrieval a workspace resource that an Agent block can call during reasoning. Knowledge bases sit alongside workflow logic and other tools, including tools exposed through an MCP server, rather than requiring a separate vector-store integration built around one LLM application. Compared with an application-centered Dify setup, Sim places retrieval inside an agent-native workspace where multiple workflow steps can use the same grounded context.

Sim's Apache 2.0 licensing also supports self-hosting, which gives you control over the agent runtime and retrieval infrastructure. Agentic RAG still costs more than a single retrieval pass because every retry adds model work and latency. You can limit reasoning depth, cache common searches, and rerank retrieved passages when response time or usage cost requires tighter bounds.

RAG's real tradeoffs

RAG can produce a weak answer even when the source documents contain the right facts. Chunk boundaries can separate a claim from its context, while a poorly matched embedding model can retrieve related but irrelevant passages. You should evaluate retrieval separately from generation because a fluent model can conceal a poor retrieval result, which is one reason agent observability matters in production.

Each retrieval step adds search, network, and prompt-processing time before generation begins. Even millisecond-scale retrieval overhead can accumulate, especially when an agent performs several searches. Caching common queries can reduce latency, but cached results may sacrifice freshness.

A RAG index also needs an explicit update policy. Knowledge bases lose relevance without continual updates, so synchronization jobs must capture changed and deleted source material. Versioned indexes can help you test updates before they affect production answers.

Vector stores extend the security boundary around private data. You should encrypt stored data and restrict retrieval according to the requesting user's permissions. An agent must never receive a chunk that the user could not open in its source system.

Production RAG requires measurable standards for retrieval accuracy and response time. Update schedules and access controls need the same deliberate planning.

Next step: build a RAG-grounded agent

An agent should use retrieval as a workspace capability whenever its reasoning requires private or current information. Sim's native Knowledge Bases give Agent blocks access to grounded context during a workflow, without requiring a separate vector-store integration tied to one chat application.

You can create the workflow with Mothership, inspect and edit its logic in the visual builder, or connect it through the API. Start building a RAG-grounded agent in Sim.