How LLM, RAG, and AI Agents Work Together

Large Language Model (LLM) – the Brain

LLMs provide the core reasoning, natural‑language understanding and generation capabilities of an AI system. Examples such as ChatGPT, Gemini or other chat‑style applications are essentially interfaces to an LLM. The knowledge encoded in an LLM is frozen at the moment the model finishes training; a model released in May 2024 only knows information up to that date. Consequently, when asked about events after the cut‑off the model can only hallucinate or give outdated answers.

Retrieval‑Augmented Generation (RAG) – the Memory System

RAG connects the static LLM brain to an external, up‑to‑date knowledge base. When a user query arrives, the RAG pipeline first searches the external store, selects the most relevant documents, and injects those passages as context for the LLM to generate its answer.

Typical workflow:

Receive user question.

Execute a retrieval step (vector search, keyword search, web search, etc.).

Rank the retrieved documents and extract the top‑k snippets.

Append the snippets to the prompt sent to the LLM.

LLM generates a response grounded in the retrieved evidence.

Because the retrieved data can be refreshed continuously, RAG gives the LLM dynamic access to fresh information without retraining the model. This improves factual accuracy, provides auditability (each answer can be traced to the source documents), and reduces hallucinations.

Example: In DeepSeek, enabling the “web‑search” option allows the system to fetch current weather pages for “Beijing weather tomorrow” and answer correctly. When the option is disabled, DeepSeek admits it knows the intent but cannot fulfill the request.

AI Agent – the Action Layer

Even with a reasoning brain (LLM) and a memory store (RAG), the system remains passive. An AI Agent adds the ability to perceive goals, plan multi‑step procedures, execute actions in the external world (e.g., API calls, file operations, sending emails), and reflect on the outcomes.

Typical agent loop:

while not goal_achieved:
    observation = perceive_environment()
    plan = LLM.generate_plan(observation, goal)
    execute(plan)
    feedback = evaluate_result()
    update_state(feedback)

Agents can therefore perform complex workflows such as:

Research a topic → extract data → generate a report → email the report.

Detect a bug in code, generate a fix, run tests, and submit a pull request automatically.

Typical Use‑Cases and Selection Guidance

LLM only : Suitable for pure language tasks such as drafting text, summarising documents, or explaining concepts where up‑to‑date factual correctness is not critical.

LLM + RAG : Use when precise, current information is required—e.g., querying internal knowledge bases, technical manuals, or recent news. RAG supplies the latest evidence to the LLM.

LLM + RAG + AI Agent : Required for autonomous workflows that involve decision‑making and external actions, such as building end‑to‑end pipelines in tools like Coze or n8n, generating multimedia content from a book, or any multi‑step process that must be executed without human intervention.