RAG: The Secret Behind AIs That Truly Understand Your Business

Imagine asking an AI assistant a question, and it responds with up-to-date information from a report that only you have. Sounds like magic? It’s not—this is RAG in action. RAG, or Retrieval-Augmented Generation, is a technique that allows AI models to query custom data sources—such as company-specific documents like PDFs, spreadsheets, databases, or even APIs—to generate more accurate and up-to-date answers.

In this article, you’ll learn in simple terms how this works and how technologies like LlamaIndex, LangChain, and CrewAI are making this idea a reality that has gone far beyond the hype.

 

Why do we need RAG?

Large Language Models (the famous LLMs, such as GPT-4, Gemini, Claude, DeepSeek, among others) already know how to answer a multitude of questions, but there is a limit: they only know what was included in their training and in the free versions up to a certain date. This means that a traditional LLM cannot answer based on your company’s internal documents or the most recent data. This is where RAG comes in: it allows the model to search for the information you need, at the moment you ask.

 

How does RAG work?

The process is simpler than it seems. Here’s how it works:

1. Data is loaded documents, PDFs, spreadsheets, websites, databases, and APIs, whatever you need.
2. It’s organized and indexed, which makes it easier for the AI ​​to search for information more quickly.
3. When you ask a question, the system consults the index and retrieves the most relevant excerpts; sends this information along with your question to the AI ​​model; generates a personalized answer, using both what the model already knows and what it just consulted.

Figure 1 demonstrates this entire process in a very didactic way.

 

This approach is especially useful for:

• intelligent corporate chatbots;
• legal assistants that consult legislation and case law;
• customer service tools with access to company documents;
• brand monitoring and market analysis systems.

Here at Loxias, we developed Loxias Live, a chatbot that has two main objectives:
1. Market analysis: generate data analysis and insights for decision-making based on public conversations and market indicators, combining Social Listening with Data Intelligence.

2. Brand-based content generation: support the production of content for social networks based on trend intelligence and good market practices, combining Social Listening with Market Intelligence.

Stages of a RAG system

While the magic of RAG may seem to happen in seconds when you ask a question, there is a well-structured process behind it, which typically follows 6 key steps. Understanding these steps helps you understand how AI can query your data and generate quality answers.

The Figure illustrates the 6 steps of RAG.

Figure 2: 

 

Here’s a description of each of these steps:

1. Loading: it all starts with importing the data. This data can come from a variety of sources (text files, PDFs, spreadsheets, websites, databases, or APIs).
2. Splitting: once the data is loaded, the next step is to split large documents into smaller chunks. This is necessary because AI models have a limit on how much text they can “see” at once (the so-called context window). Splitting the content into smaller chunks (such as paragraphs or blocks of text) ensures that the model can process this information efficiently and generate more accurate answers. It also makes searching much easier, because when a question is asked, the system only retrieves the relevant chunks.
3. Indexing: now, the chunks of text go through an indexing step. This means creating numerical representations of the content called “vector embeddings” that capture the meaning of the data.
4. Storing: once indexed, the data is stored in an appropriate index (such as a vector database). this prevents the need to reprocess everything again in the future and ensures that the system is ready to respond quickly to queries.
5. Querying: when a user asks a question, the system queries the index and retrieves the most relevant pieces of text. These pieces are then passed to the AI ​​model, along with the question, to generate an answer that combines general knowledge with specific information from your data.
6. Evaluation: finally, it is important to evaluate the effectiveness of the system. Metrics such as accuracy, relevance, and speed help you understand whether the system is meeting expectations. Continuous evaluation allows you to adjust and improve performance over time.

 

Architecture of a RAG system

Now that you’ve learned the detailed steps of a RAG system, it’s worth reinforcing the concept with a practical view of the typical architecture of these systems. This helps you visualize how everything works “under the hood.” A RAG system usually has two major components: Indexing and Retrieval/Generation.

 

Indexing

 

This is the preparation of data, done before users query it. It usually occurs in an offline process. The steps involved are:

• Load: import data from documents, websites, APIs, etc.
• Split: break large documents into smaller, manageable chunks.
• Store: save these chunks in a vector index to facilitate future searches.

 

You can think of the index as a “structured memory” that the system can consult when you ask a question.

 

Retrieval/Generation

 

This is where the magic happens, when the user interacts with the system. The steps are as follows:

• Retrieve: when a question arrives, the system queries the index and searches for the most relevant pieces of text.
• Generate: the AI ​​model (LLM) receives the question plus the retrieved pieces of text and generates a personalized and informed answer.

 

This architecture ensures that the AI ​​can produce answers based on its own data and not just what was learned during the original training of the model.

Figure 3 illustrates the architecture behind RAG.

Figure 3:

 

Tools that help create RAG systems

LlamaIndex

LlamaIndex (previously called GPT Index) is an open source framework created by Jerry Liu that facilitates the integration of language models (LLMs) with private and unstructured data, such as PDFs, documents, spreadsheets, databases and APIs. Its main objective is to implement the RAG (Retrieval-Augmented Generation) technique, where the language model responds based on relevant data retrieved from external sources. The main features of LlamaIndex are:

• Data connectors: connectors to import data from sources such as Notion, Google Drive, PDFs, APIs, etc.;
• Intelligent indexing: builds indexes (such as VectorStoreIndex, TreeIndex, ListIndex) for efficient querying;
• Custom retrieval: retrieves only the relevant excerpts for each question;
• Integration with LLMs: Works with OpenAI, Anthropic, Cohere, among others;
• Memory and Chat Engines: can keep conversation history and build workflows with chatbots.

 

LangChain

It is an open source framework in Python and Java, developed to facilitate the creation of applications that use Large Language Models (LLMs) in an advanced way, integrating them with external data, tools and contextual memory. It was created by Harrison Chase, and its main focus is to transform LLMs into useful and interactive applications, such as assistants, autonomous agents and RAG (Retrieval-Augmented Generation) systems. To help with understanding, while a model like GPT-4 or Gemini can answer questions based on text, LangChain helps to:

• Connect the model to databases or corporate documents;
• Create memory so that it remembers previous interactions;
• Use external tools, such as browsers or APIs;
• Build decision flows, “if this happens, do that”;
• Build agents that can reason step by step and take actions.

 

The main components of LangChain are:

• LLMs: integrates models such as OpenAI, Anthropic, Cohere, Hugging Face, Gemini, among others;
• Chains: execution flows that combine several steps, such as prompt + response;
• Agents: models with freedom to choose actions based on tools;
• Memory: allows the system to “remember” previous interactions;
• Retrievers: use mechanisms such as FAISS, Chroma or Elasticsearch to search for data;
• Toolkits: ready-made sets for tasks such as search, translation, scraping, etc.

 

In practice, you can use LangChain to create a legal assistant with GPT-4 that:

1. Searches for legal documents in PDFs saved in a directory;
2. Extracts and summarizes the relevant parts;
3. Answers questions based on this content;
4. Keeps a memory of the conversation with the user.

 

In addition, with LangChain, you can chain these steps and even include agents to make decisions, such as: “I didn’t find it in the PDF, so I’ll consult an external API.” Popular use cases:

• RAG (querying PDFs, websites or databases with LLMs);
• Chatbots with persistent context;
• Multi-step automations and decision making;
• Autonomous agents with integrated tools (browser, APIs).

 

CrewAI

CrewAI is an open-source Python framework for orchestrating autonomous AI agents that collaborate to perform complex tasks. Ideal for developers and companies looking to automate processes with multiple specialized agents, CrewAI lets you create “crews” — teams of agents with specific roles, goals, and tools — that work together in a coordinated manner.

 

Developed by João Moura, a Brazilian guy, CrewAI is a lightweight and fast platform, built from scratch, without dependence on frameworks such as LangChain. Its focus is to allow agents based on Large Language Models (LLMs) to act autonomously and collaboratively, each with specific functions, such as researcher, writer or analyst. These agents can use external tools, APIs and shared memory to achieve defined goals. CrewAI has the following features:

• Agents with defined roles: each agent has a specific role, objective and history, allowing specialization and efficient collaboration;
• Orchestration with Flows: in addition to “crews”, it is possible to define “flows” — event-driven workflows, with detailed control over task execution, conditional logic and state management;
• Integration with LLMs and APIs: compatible with several language models and external services, facilitating adaptation to different needs;
• Local or cloud execution: flexibility to run agents locally, on own servers or in the cloud, depending on the available infrastructure.

 

Examples of use cases:

• Business process automation: creating teams of agents to perform tasks such as event planning, financial data analysis, or customer service;
• Content generation: agents specialized in research, writing, and review collaborate to produce white papers or in-depth reports;
• Brand monitoring: using multiple agents to collect and analyze information from different platforms, providing insights into the brand’s presence.

 

Prompt Engineering: The Secret to Good Answers

Finally, it’s worth remembering that how you ask matters a lot. Prompt Engineering is the practice of writing questions and instructions in a way that the AI ​​model correctly understands what you want and produces useful answers.

 

 

Conclusion

The combination of LLMs with the Retrieval-Augmented Generation technique is revolutionizing the way companies use AI. Now, it’s not just a robot that “knows everything about the internet,” but an intelligent assistant that consults its own documents and responds with personalized, up-to-date information.

If you want to turn the potential of LLMs into something truly useful for your business, RAG is an essential path to explore — and frameworks like LlamaIndex, LangChain, and CrewAI are making it increasingly easy.

 

 

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