RAG (Retrieval-Augmented Generation)

An overview of the essential steps to build a RAG for the development of intelligent, context-aware assistants.

09 Nov 2025

RAG (Retrieval-Augmented Generation) agents are AI agents that autonomously integrate external information into a Large Language Model (LLM), allowing it to access and use data that was not part of its original training.

In other words, while a traditional LLM responds only based on what it learned during training, a RAG agent can search for up-to-date and specific information from external sources before generating a response.

This type of agent is especially useful in several contexts, such as:

• Corporate onboarding: an assistant that helps new employees understand internal policies and company processes.
• Technical support: a chatbot capable of answering complex questions about products or systems, based on official documentation.
• Personalized virtual assistant: an agent that understands an organization’s context and responds with precise and updated information.

The steps to create a RAG agent

RAG agents can be developed and integrated using programming languages such as Python or Node.js, usually with the help of specialized frameworks like LangChain or LlamaIndex. It is also possible to build these workflows without writing code, using visual tools such as Langflow. The building process usually follows these main steps:

Setup of the knowledge base

The first step is to define where the agent will obtain its information. This data can come from documents (PDF, DOCX, TXT, etc.), databases, APIs, or even websites. The documents are converted into plain text and then divided into small blocks called chunks, smaller pieces of text (for example, 300 to 1000 characters) that make search and indexing easier.

Embeddings and vector storage

Each chunk is transformed into an embedding, meaning a numerical vector representation of the text’s meaning. These vectors are essentially “mathematical translations” of the words, allowing the system to understand semantic similarity. For example, embeddings for “dog” and “pet” will be close in vector space because they are semantically related. These embeddings are then stored in a specific type of database called vector database, such as ChromaDB or Pinecone.

Query and response generation

When the user asks a question, the backend (in Python or Node.js) sends the query to the vector database, which searches for the most relevant chunks based on semantic similarity to the question.

These retrieved excerpts are then sent to the LLM along with a prompt, a set of instructions that guides the model on how it should respond.

There are currently several LLMs available on the market that can be used in this process, such as OpenAI (GPT-4, GPT-4o), Anthropic Claude, Gemini (Google), Mistral, and LLaMA (Meta).

Each of these options can be connected to frameworks like LangChain or LlamaIndex, allowing the application to choose the most suitable model depending on the use case, cost, or privacy needs.

The prompt is not just the user’s question, it is the central component that defines the LLM’s role, tone, and behavior. Through prompt engineering, it is possible to guide the model to generate responses that are more relevant and aligned with the agent’s goal.

For example, we can instruct the model to:

• Adopt a specific role: “You are a technical assistant specialized in software documentation. Provide an afirmative response based only on the current information.”
• Define the response style: “Explain briefly and in short bullet points.”
• Control the output format: “Return the answer in JSON format with the fields ‘answer’ and ‘sources’.”

This practice is essential to ensure that the LLL correctly uses the retrieved data and maintains clarity, coherence, and accuracy in the final response.

In summary, the combination of semantic search, prompt engineering, and advanced language models such as GPT-4, Gemini, or Claude enables RAG agents to deliver contextually rich, updated, and personalized answers, even when the base model never had direct access to that information during training.

Visual tools and accessible alternatives

For those without programming experience, Langflow provides a visual interface based on blocks, where we can build the entire RAG agent workflow, from loading documents to configuring the language model.

A more recent alternative is LangGraph, an extension of LangChain focused on orchestrating complex agent workflows. It allows the creation of agents that interact with one another, share context, and perform more elaborate tasks while maintaining full control over the process state and transitions.

LangGraph is especially useful for building autonomous, multi-step assistants that need to plan actions or query different sources in sequence.

Conclusion

With frameworks like LangChain, accessible vector databases, and visual tools such as Langflow or LangGraph, any developer or even users without coding experience can create intelligent agents capable of delivering personalized, contextualized, and always up-to-date responses.