A practical how-to guide for RAG implementation

Let’s be honest, fresh out of the box, Large Language Models (LLMs) are brilliant but clueless about your company. They don’t know your products, your policies, or your customers’ common issues. When they try to answer support questions, they’re often just guessing. This can lead to vague answers or, even worse, "hallucinations" where the AI just makes stuff up.

This is where Retrieval-Augmented Generation (RAG) comes in. Think of RAG as giving your AI a library card to your company’s private collection, all your internal knowledge, so it can look up the right answer before it speaks.

This guide will walk you through the real steps of a RAG implementation, from hooking up your data sources to generating accurate, helpful answers. We’ll break down the techy concepts into plain English and show you how to sidestep common traps, whether you’re building it yourself or using a platform to get it done faster.

What you’ll need for a successful RAG implementation

Before jumping in, it helps to know the main parts of a RAG system. Even if you use a platform like eesel AI that handles all of this for you, understanding the moving pieces makes it clear what’s happening behind the scenes.

• Knowledge Sources: This is all the stuff your company knows. We’re talking help center articles, past support tickets, internal wikis like Confluence or Notion, Google Docs, and even product info from your Shopify store.

• An Embedding Model: Think of this as a translator. It turns all your text-based knowledge into a special numerical format (called vectors). This lets a computer grasp the meaning and context of your documents, not just the keywords.

• A Vector Database: This is a special kind of database built to store and search through those numerical vectors. It’s what allows the system to find the perfect snippet of information to answer a question in the blink of an eye.

• A Large Language Model (LLM): This is the part of the system that does the "talking." It takes the user’s question and the information found by the vector database and crafts a natural, human-like response.

• An Orchestration Layer: This is the traffic cop that directs everything. It manages the whole process from the moment a question comes in, to finding the right info, sending it to the LLM, and delivering the final answer back to the user.

A step-by-step guide to your RAG implementation

Putting a RAG system together follows a pretty logical sequence. Here’s how it works, with a look at the headaches of doing it yourself versus how a specialized platform can make your life easier.

Step 1: Prepare and connect your knowledge sources

First things first, you need to round up all the information you want your AI to learn from. This means pointing it to all your different knowledge spots.

The process:

You’ll need to pull data from all over the place. For example, you might have to connect to your Zendesk or Freshdesk API for old tickets, scrape your public help center, and set up connections for internal docs in Confluence or Google Docs. You also need a way to keep this data synced up, otherwise the AI will be giving out old news.

The challenge:

This is where things can get messy, fast. Each data source has its own API and format, which means writing custom code for each one just to pull the information out and clean it up. Building a data pipeline that reliably keeps everything in sync isn’t a weekend project; it can easily tie up an engineer for weeks or months.

The eesel AI advantage:

This is where you can shrink that timeline from months to minutes. eesel AI offers over 100 one-click integrations. Instead of having an engineer build custom connectors, you just log into your accounts. It automatically pulls in and learns from your past tickets, help articles, and wikis, creating one central brain that’s always up-to-date. You can genuinely get a smart AI agent running in a few minutes.

Step 2: Chunk and embed your data

Once you have all your data, you can’t just dump it on the LLM. You have to break it down into smaller, logical pieces (or "chunks") and then run them through the embedding model to turn them into vectors.

The process:

You’ll have to decide on a chunking strategy. Do you split your documents by paragraph? By a certain number of words? Or a more complex method? After you’ve chopped it all up, you feed the text to an embedding model and store the vectors it spits out in your vector database.

The challenge:

How well you chunk your data has a huge effect on your results. If you do it poorly, the AI might pull up snippets that are missing key context or are just plain irrelevant. Managing the whole embedding process and keeping the vector database running also demands some niche technical skills.

The eesel AI advantage:

eesel AI takes care of all this for you. It intelligently chunks your documents using a method that’s already been fine-tuned for customer support and internal knowledge, making sure the AI finds the most useful information to give a complete and accurate answer.

Step 3: Set up the retrieval process

This is the "retrieval" part of RAG. When a user asks a question, the system turns their question into a vector and searches the database for the text chunks with the most similar vectors. These are the most relevant pieces of information.

The process:

You have to build a search function that can take a user’s question, embed it, and then run a similarity search in your vector database. This function should then pull the top few most relevant documents to use as context for the answer.

The challenge:

Getting the retrieval system tuned just right is both an art and a science. You can end up pulling up the wrong manual or an outdated ticket if you’re not careful. It often takes a lot of trial and error, and maybe even some data science know-how, to get the search accuracy where it needs to be.

The eesel AI advantage:

The retrieval system in eesel AI is already built and optimized for support and IT service management. It’s designed to find and prioritize the most helpful information, so you don’t have to do any manual tuning.

Step 4: Configure the generation and workflow

Now that you have the right context, the final step is to hand it over to the LLM, along with the original question, to generate the final answer. This is where you teach the AI how to behave and what it’s allowed to do.

The process:

This is where you’ll use prompt engineering, basically, writing instructions for the LLM. You’ll tell it what tone of voice to use, order it to stick to the information you provided, and create rules for when it should give up and escalate to a human. You might also want to build a workflow that lets the AI do things, like add a tag to a ticket or look up an order status.

The challenge:

Writing good prompts and building a flexible workflow engine is tough. It’s hard to get the AI’s personality right while putting up the necessary guardrails. And if you want it to perform custom actions, that usually means a lot more development work. Most built-in helpdesk AIs give you very little control here, locking you into their way of doing things.

The eesel AI advantage:

eesel AI gives you a completely customizable workflow engine with a powerful prompt editor. You can define your AI’s persona, set very specific automation rules, and set up custom actions that can interact with other systems or update ticket details, all without writing a line of code.

Pro Tip: With a tool like eesel AI, you don’t have to boil the ocean. Start by automating responses for one or two simple, common ticket types. Once you see it working and get comfortable, you can slowly expand what the AI handles. It’s a much smoother way to roll it out.

Step 5: Test and deploy with confidence

You wouldn’t want an untrained employee talking to your customers, and the same goes for an AI. You need to make sure it actually works before you set it live.

The process:

If you’re building this yourself, you’ll need a separate testing environment. You’d have to put together a bunch of test questions to see how the AI performs, which can be a slow, manual process. And there’s no real way to know if your tests cover all the weird and wonderful ways your customers ask for help.

The challenge:

How do you know if your test cases are any good? It’s really hard to predict how much time and money the AI will actually save you before you flip the switch.

The eesel AI advantage:

eesel AI has a neat feature for this: its simulation mode. It lets you test your AI setup on thousands of your real past support tickets in a safe environment. You can see exactly how it would have responded to each one, get a solid forecast of your deflection rate, and tweak its behavior before a single customer ever talks to it. It takes the guesswork and risk right out of the equation.

Common pitfalls in RAG implementation (and how to avoid them)

A RAG system can be a huge help, but there are a few common issues that can trip you up. Here’s what to look out for.

• Garbage in, garbage out: Your AI is only as smart as the information you give it. If your help docs are wrong, outdated, or just confusing, the AI is going to give bad answers.

• How to avoid it: Make a habit of auditing and cleaning up your knowledge base. Some tools can even help with this. For instance, eesel AI can automatically draft new knowledge base articles based on how human agents successfully resolved tickets, helping you fill in the gaps with proven solutions.

• Stale information: Things change quickly. If your RAG system isn’t constantly syncing with your latest documents, it will start giving out old information and causing confusion.

• How to avoid it: Make sure your data sync is automated. Platforms like eesel AI are built for this; they constantly keep the AI’s knowledge fresh without you having to lift a finger.

• Security and privacy oversights: You’re feeding your company’s private data into this system. You have to be absolutely sure about how that data is stored, who can see it, and that it isn’t being used to train some other company’s AI model.

• How to avoid it: Go with a solution that takes security seriously. eesel AI guarantees your data is never used to train general models, offers EU data residency, and works with SOC 2 Type II-certified partners.

• Watching out for surprise costs: A DIY RAG implementation can come with some scary bills. You have to pay for LLM API calls, vector database hosting, and all the computing power needed for the system to run. These costs can balloon unexpectedly, especially if you’re working with a vendor that charges you every time the AI successfully resolves a ticket.

• How to avoid it: Choose a platform with clear, predictable pricing. eesel AI’s plans are based on a flat monthly fee for a certain volume of AI interactions. There are no per-resolution fees, so you always know exactly what you’ll be paying.

Step-by-step guide to building RAG

Implementation doesn’t have to be a months-long project

So, that’s the roadmap for getting a RAG system up and running. It’s a technology that can turn a generic chatbot into an expert assistant that actually understands your business. While building it yourself is possible, it’s a major project that requires specialized skills in data engineering, machine learning, and security.

But you don’t need a whole AI team to get all the benefits. Platforms like eesel AI are designed to handle all that complexity for you. They provide a simple, self-serve way to go live in minutes, not months. By taking care of everything from data connections and chunking to testing and workflows, eesel lets you focus on what you do best: delivering great support.

Get started with your RAG implementation in minutes

Ready to see what a RAG-powered AI agent can do for your business?

1. Connect Your Knowledge: Sign up for eesel AI and connect your help desk and knowledge sources in just a few clicks.

2. Simulate Performance: Run a simulation on your past tickets to see exactly how many inquiries you can automate.

3. Go Live: Launch your AI agent and start saving time and making customers happier today.