It’s no secret that Large Language Models (LLMs) are amazing at so many different tasks. Who doesn’t use them these days to generate text, answer questions, or brainstorm ideas? But their power goes beyond quick interactions. When integrated into a well-designed workflow, they can even help automate entire business processes.

One challenge is that LLMs only know what they were trained on. Ask about new information, company-specific data, or very detailed knowledge, and they might guess, or worse, make things up.

Retrieval-Augmented Generation (or RAG for short) comes to the rescue! Instead of relying only on the model’s internal knowledge, RAG adds a retrieval step. It gets the most relevant documents or data first, and then the LLM uses that context to generate grounded, accurate responses.

In this article, we’ll explain what RAG is, why it’s important, and then walk you step by step through building a RAG workflow using StackAI.

Let 's get into it!

What Is RAG and Why Is It Important?

RAG is a technique that combines two parts: retrieval and generation.

1. Retrieval – searching for the most relevant information.

2. Generation – using a language model to generate an answer based on that information.

Think of it like this:

• A student answering a test from memory = an LLM alone.

• A student allowed to use their notes and books while answering = RAG.

The second student (RAG) can give more accurate, up-to-date answers because they don’t just rely on what they remember — they also pull from reliable references.

How RAG Works:

It usually starts when a user asks a question or inputs a query - although this can be also done by the system itself when an action is triggered. Instead of the language model relying only on what it was trained on, the system first goes out and searches through external data sources — these might be PDFs, excel spreadsheets, word documents, or even databases.

1. From those sources, the system retrieves the most relevant pieces of information.

2. Next, those retrieved snippets are fed into the language model along with the original question or query. The LLM blends its built-in knowledge with this new, context-rich information.

3. Finally, the model generates a response that feels natural and conversational but is also grounded in factual, up-to-date data. The user gets an answer that is accurate, relevant, and tailored to their needs.

Now, when you build a RAG system, there are a few key pieces working together behind the scenes. 

First, your information needs to be loaded and broken into smaller sections so they’re easier to search. These smaller sections are called chunks. Then those chunks are stored in a special way, using vector embeddings, inside what’s known as a vector database. A retriever searches through the vector database, and once the right pieces are found, the language model takes over. It blends the retrieved information with what it already knows to generate a clear, accurate answer.

Why RAG Matters

Instead of relying only on what the model was trained on, RAG brings in your own data at the moment of a query. This means:

• More accurate answers – reduces “hallucinations” from the LLM.

• Up-to-date information – you can add your own latest info to the system.

• Contextual responses – answers that reflect your own data.

Because of these advantages, RAG is especially useful for things like customer support chatbots, internal knowledge assistants, document search and summarization, and any workflow where factual accuracy really matters.

👉 In the next section, we’ll introduce Stack AI and explain why it’s one of the easiest ways to build RAG workflows, even if you’re not a dev!

Overview of Stack AI for RAG Workflows

Building a RAG workflow from scratch can be complex. Normally, you’d need to:

• Clean and load your documents.

• Split them into chunks.

• Create embeddings and store them in a vector database.

• Set up a retriever.

• Connect everything to a language model.

• Add governance rules (like filters or usage limits and more) manually.

That means writing quite a bit of code, managing infrastructure, and stitching together multiple tools. For many teams, this is time-consuming and requires specialized engineers.

Stack AI takes a different approach. It’s a local, end-to-end, low/no-code platform designed to make RAG workflows easier and faster to build!

Key Benefits of Using Stack AI

End-to-End Platform: Everything you need — from document ingestion to retrieval, generation, and deployment — is in one place. No need to manage many different tools.

1. Low/No-Code: With Stack AI’s drag-and-drop interface, you don’t have to write complex pipelines in Python. Non-technical teams can create and test RAG workflows themselves.

2. Governance and Control: Companies need security and compliance. Stack AI includes governance features like access controls, monitoring, and data privacy management, making it safer for sensitive business data.

3. Speed and Efficiency: What used to take weeks of coding and infrastructure setup can now be done in hours. For example, a customer support team could build a chatbot over their internal manuals in a single afternoon.

4. Easy Deployment: Once your workflow is ready, deploying it is straightforward. You can publish it as an API, embed it in a chatbot, or roll it out as an internal tool — all without heavy engineering effort.

5. Scalable: Start small with one use case, then expand to more complex workflows across departments. Stack AI grows with your needs. 

Stack AI makes RAG practical for businesses. It gives you the accuracy and power of RAG without the headaches of coding, infrastructure, or compliance risks.

Step-by-Step: Building a RAG Workflow in Stack AI

To show how easy it is to create a RAG workflow in Stack AI, let’s walk through the process of connecting your data to an LLM based agent.

To illustrate this process, let’s imagine you’re an HR manager rolling out new policies. Employees constantly email with questions like “How many vacation days do I have left?” or “What’s the parental leave process?”

Instead of your HR team spending hours replying, you upload all HR docs, policies, and FAQs into Stack AI. With a RAG workflow:

• Employees would ask questions directly in chat.

• The retriever would pull the exact section of the HR handbook.

• The LLM would answer in plain, conversational language.

👉 Result: HR saves time, employees get instant and accurate answers, and no one has to hunt through 50-page PDFs.

Let me guide you step by step!

Step 1: Create a Workflow

First things first, in the dashboard, we are going to start in the workflow builder.

Click create and then, select knowledge base agent and click template.

We need to give our project a name. For this particular example, we are going to call our project HR Assistant. When you are done giving your project a name, click create project.

Once you have created your project, you’ll see your RAG pipeline laid out visually as a sequence of connected blocks. Each block represents a step in your workflow. Let’s have a closer look at each one:

• User Message (Input Node): This is the starting point where a user types their question or request.

• Documents (Knowledge Base Node): This node stores and searches your uploaded documents or connected data sources. It retrieves the most relevant sections and passes them forward.

• AI Model (LLM Node): The large language model (e.g., Claude, GPT, or another model) takes the user’s question or request along with the retrieved content and generates a clear, natural response.

User Output (Response Node): The system returns the final answer to the user.

Because everything is laid out visually, you can see exactly how information flows through the system — from the user’s query, to the knowledge base, through the model, and back as a response. This makes it easy to understand, adjust, and expand your RAG workflows without needing to code.

In our HR Assistant example, we can expect the flow to be simple and easy to follow:

• User Message (Input Node): This is where the employee will type their question, such as “How many vacation days do I get?”.

• Documents (Knowledge Base Node): The uploaded HR policies will live here. This node will search the relevant documents and pass the most relevant sections forward.

• AI Model (LLM Node): In this case, we’ve connected Claude 3.5 Sonnet from Anthropic. The model takes both the employee’s question and the retrieved policy text, then generates a clear answer.

User Output (Response Node): Finally, the answer is returned to the employee in plain text. For example, we should expect something like this: “You’re entitled to 25 days of paid vacation per year, with up to 5 days carried over into the next year.”

This drag-and-drop interface means you don’t have to worry about coding pipelines or wiring up APIs manually. You can literally see your RAG workflow end to end — from the employee’s question all the way to the AI’s response!

Step 2: Add a Knowledge Base

Stack AI lets you connect to many sources: static documents (PDFs, Word, CSVs), websites, databases, APIs, or corporate file servers like Google Drive, SharePoint, or Dropbox.

For a simple start, we are going to drag and drop our documents to the Documents node into our workflow. Stack AI will automatically prepare it for retrieval by indexing and chunking the content so you don’t have to worry about that.

For our example,let’s say HR wants to reduce repetitive emails about leave policies. We upload two documents:

• HR_Vacation_Policy.docx (with vacation and sick leave rules).

• HR_Parental_Leave_Policy.pdf (with parental leave and work-from-home policies).

Step 3: Configure Indexing and Chunking

Once your documents are  uploaded, you can further configure how they are broken down if you wish to do so:

• Chunks – how the text is split into smaller sections.

• Metadata filtering – tags to help the AI search more efficiently.

• Other parameters – how many results to retrieve, ranking strategies, etc.

Step 4: Connect the LLM to the Knowledge Base

Next, connect the knowledge base node to your LLM node. This way, whenever the model receives a question, it can access the relevant chunks of your uploaded data.

For example, if a user asks: “What is the company’s parental leave policy?”, the model will look not only at the question but also at the HR document sections before generating a response. So the LLM will use the “Parental Leave Policy” chunk and generate a clear response like: “Employees are entitled to 16 weeks of paid parental leave. You need to notify HR at least 8 weeks before your intended leave start date.”

To do this, click on the LLM node. Here, you can choose from different model providers. To connect your documents to the LLM, click Add Knowledge Base and select the one you want to use. In this example, we’ll select the documents prepared for the HR Assistant.

Step 5: Add Guardrails

When connecting an LLM to your workflow, it’s important to make sure the system responds safely and responsibly. Stack AI allows you to enable guardrails — built-in controls that filter or block inappropriate outputs.

In this example, we’ve toggled on three guardrails:

• Toxic Content: Prevents the model from producing harmful, offensive, or abusive language.

• Legal Advice: Makes sure that the assistant does not provide legal guidance beyond the scope of approved company policies.

• Suicidal Thoughts: Detects and safely redirects sensitive queries related to self-harm or crisis situations.

These guardrails are especially important in enterprise settings, and often gets overlooked. But when you turn them on, you reduce the risk of misuse and make sure the responses always stay within professional and ethical boundaries.

Step 6: Give the LLM instructions

The next step is to decide how you want your AI to behave. Inside the LLM node’s Prompting panel, you can set the tone, style, and guidelines for how the model should respond. This is where you tell the AI if it should be formal or casual, concise or detailed, and how it should use the knowledge base when answering questions.

Clear instructions here are important because they make sure your assistant stays consistent, polite, and aligned with your organization’s goals.

For our HR Assistant the prompt tells it to always use both the employee’s question and the most relevant information from the HR documents, provide clear, accurate, and helpful answers, keep responses brief, polite, and easy to understand and to maintain a conversational and friendly tone, as if it were an HR colleague.

So when employees interact with the system, they receive not just correct answers, but also responses that feel approachable and aligned with company culture.

Step 7: Test your workflow

Once you’ve completed these steps, your RAG workflow is ready! You can now ask natural language questions, the retriever will pull the right context from your documents, and the LLM will generate accurate, business-specific answers.

In order to do this, just write a question or query on the Question block,  hit run and see the magic happen:

In our example, we have tested question: “Can I work from home three days a week?”

So what happened behind the scenes?

• The User Message node captured the question.

• The Documents node retrieved the relevant section from the HR policy file.

• The AI Model node combined the question with the retrieved context.

• The User Output node displayed a clear, accurate response, citing the Work From Home Policy document.

The assistant’s answer confirms that employees can work from home up to three days a week with manager approval, showing both accuracy and alignment with company policy.

This is how easy you can validate that the RAG workflow is functioning as expected before publishing it for real users!

Step 8: Deploy

Once your workflow is working as expected, the final step is to make it available to real users. In Stack AI, this is done through the Export tab, where you can choose how people will interact with your assistant. 

You’ll see several interface options, including:

• Form – a simple form-based interface.

• Chat Assistant – a standalone chat interface.

• Website Chatbot – embed the assistant directly into your website as a chatbot widget.

• Batch – process multiple inputs at once.

• Slack App – integrate directly with Slack.

• Microsoft Teams – integrate directly with Teams.

• And more!

For this example, we’ve chosen the Website Chatbot interface. This allows employees to interact with the HR Assistant in a familiar chat-style window right from the company intranet.

From there, you can:

• Give your assistant a name and description.

• Add a disclaimer message (e.g., “AI assistants might make mistakes. Check important information.”).

• Customize the input placeholder (“How can I help you today?”).

• Preview what the chatbot will look like before publishing.

The chatbot responds directly to user queries, pulling information from the HR knowledge base and citing the relevant documents. For example, when asked “How many vacation days do I get each year?”, the assistant retrieves the vacation policy and gives a grounded answer with references.

The best part? Deployment is just a couple of clicks away — no complex integrations or coding required. You can drop it into your website, connect it to Slack or Teams, or even expose it as an API. How cool is that?

Final Thoughts

RAG is one of the most effective ways to make large language models more accurate, up-to-date, and useful for real business cases. When you combine retrieval with generation, you get responses that are grounded in your own data — not just what the model was trained on.

Traditionally, building a RAG system meant connecting multiple tools, writing custom code, and handling infrastructure. But with Stack AI, that complexity disappears. The platform gives you an end-to-end, low/no-code environment where you can import your data, set up retrieval, connect an LLM, and deploy a workflow — all with built-in governance and compliance features.

You can create a customer support chatbot, an internal knowledge assistant, or any AI system that needs accurate answers — and with Stack AI, you can take it from idea to production in just a few hours.

👉 Ready to get started? Try building your own RAG workflow with Stack AI today and see how easy it can be!