Build an AI That Reads Your Documents (RAG Guide)

The Confident Idiot

In our last lesson, we built a ridiculously fast AI using Groq. An intern on six shots of espresso. But this intern, for all their speed, has a fatal flaw. They’re a pathological liar.

Ask them about your company’s return policy, and they’ll invent one on the spot. “Yes, we absolutely offer a 365-day, no-questions-asked refund on all used products, including digital downloads!” They’ll say it with such confidence, such conviction, that you’ll almost believe them. Until, of course, that answer costs you a few thousand dollars in fraudulent returns.

This problem, known as “hallucination,” is the single biggest barrier to using AI for serious business tasks. A generic Large Language Model is like a brilliant new hire who has read the entire internet but has never once seen your company’s employee handbook. They’re full of general knowledge but dangerously ignorant of your specific reality.

Why This Matters

An AI that can’t be trusted is a toy, not a tool. To build real, revenue-generating automations, you need reliability. You need ground truth. You need your AI to stop guessing and start *knowing*.

Today, we’re fixing our intern’s lying problem. We’re going to give them a library of your company’s documents and teach them a simple, powerful rule: **“If it’s not in the documents, you don’t know the answer.”**

This workflow, called Retrieval-Augmented Generation (RAG), is the foundation of modern, trustworthy AI systems. It allows you to build:

Internal Help Desks that give instant, accurate answers about HR policies, IT procedures, and benefits.
Customer Support Bots that know every detail of your product manuals and can troubleshoot complex issues correctly.
Personal Assistants that can summarize your meeting notes, find details from past project documents, and never forget a thing.

We’re turning the confident idiot into a seasoned expert. Your expert.

What This Tool / Workflow Actually Is

Retrieval-Augmented Generation (RAG) sounds like something from a sci-fi novel, but it’s embarrassingly simple. It’s an open-book exam for the AI.

Instead of just asking the AI a question and hoping it knows the answer, we perform a two-step process:

Retrieve (The ‘R’): Before we talk to the AI, we first search through *our own* private documents to find snippets of text that are relevant to the user’s question. This is the “retrieval” step. It’s like a librarian finding the exact right page in a book for you.
Augment & Generate (The ‘AG’): We then take the user’s original question, combine it with the relevant text we just found, and hand it all to the AI. We give it a new prompt like: “Using ONLY the following information, answer this question.” The AI’s job is now much easier: it just has to synthesize an answer from the text we provided.

What it does: It grounds the AI’s response in a specific, trusted set of facts, dramatically reducing hallucinations and making the answers relevant to your business.

What it does NOT do: It does not “train” or “fine-tune” the model. The AI’s underlying brain isn’t changed. We are simply giving it perfect, just-in-time information to use for one specific task. When the task is over, it forgets the information, which is exactly what we want.

Prerequisites

This is where we start building, but don’t panic. The tools do most of the heavy lifting. All you need is:

Python: A working Python environment on your computer. Again, if you’re unsure, a free Replit account works perfectly.
An OpenAI API Key: We need OpenAI for their excellent “embedding” models, which are crucial for the “retrieval” step. Grab a key from your OpenAI account.
A Text File: Create a folder for our project. Inside it, create a file named company_policy.txt. This will be our AI’s knowledge base.

That’s it. No servers, no databases, just one file and a few lines of code.

Step-by-Step Tutorial: Building the Knowledge Base

Our first job is to turn our boring text file into a searchable “vector store.” Think of this as creating a hyper-intelligent index for our document library.

Step 1: Prepare Your Project

Create a main folder (e.g., rag_project). Inside it, create your Python file (e.g., build_rag.py) and your knowledge file, company_policy.txt.

Paste this into company_policy.txt:

Company Policy Document

Return Policy:
Our return policy allows for returns of unused and unopened products within 30 days of purchase. A valid receipt is required for all returns. Customers will receive a full refund to the original payment method. Digital products and sale items are considered final sale and are not eligible for returns.

Shipping Information:
Standard shipping is free for all orders over $50. For orders under $50, a flat rate of $5 is applied. Standard shipping typically takes 3-5 business days. Expedited shipping is available for a fee of $15 and takes 1-2 business days. We do not currently ship to international addresses.

Paid Time Off (PTO) Policy:
Full-time employees accrue 8 hours of PTO per month. PTO requests must be submitted to the direct manager at least two weeks in advance. Unused PTO does not roll over to the next calendar year.

Step 2: Install the necessary libraries

We’ll use LangChain, a popular framework that simplifies these workflows. Open your terminal and run:

pip install langchain langchain-openai faiss-cpu openai python-dotenv

Step 3: Write the Code to Build and Query the RAG System

Now, let’s write the Python script. Copy and paste the following into your build_rag.py file. This single script will load the document, process it, and answer a question based on it.

import os
from dotenv import load_dotenv

# LangChain components
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA

# Load environment variables from .env file
load_dotenv()

# Make sure you have OPENAI_API_KEY in your .env file or environment
if os.getenv("OPENAI_API_KEY") is None:
    print("OPENAI_API_KEY not set. Please set it in your environment.")
    exit()

# --- 1. Load the Document ---
print("Loading company policy document...")
loader = TextLoader('./company_policy.txt')
documents = loader.load()

# --- 2. Split the Document into Chunks ---
# This is important for context window management
print("Splitting document into chunks...")
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

# --- 3. Create Embeddings and Store in FAISS ---
# OpenAI's embedding models convert text chunks into numerical vectors
# FAISS is a local vector store that allows for fast similarity searches
print("Creating embeddings and building vector store...")
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(docs, embeddings)

# --- 4. Set up the LLM and the RAG Chain ---
# We are using OpenAI's gpt-3.5-turbo here
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)

# The RetrievalQA chain combines the retriever (our vector store) and an LLM
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

# --- 5. Ask a Question ---
print("--- RAG System Ready. Ask a question! ---")

question1 = "What is the return policy for sale items?"
response1 = qa_chain.invoke({"query": question1})
print(f"Q: {question1}")
print(f"A: {response1['result']}\
")

question2 = "How long does expedited shipping take?"
response2 = qa_chain.invoke({"query": question2})
print(f"Q: {question2}")
print(f"A: {response2['result']}\
")

question3 = "Can I get a refund for a used item?"
response3 = qa_chain.invoke({"query": question3})
print(f"Q: {question3}")
print(f"A: {response3['result']}\
")

# Example of a question the document CANNOT answer
question4 = "What is the company's dress code?"
response4 = qa_chain.invoke({"query": question4})
print(f"Q: {question4}")
print(f"A: {response4['result']}\
")

Step 4: Set Up Your API Key and Run

Create a file named .env in the same folder. Add your OpenAI API key to it like this:

OPENAI_API_KEY="sk-YourSecretKeyGoesHere"

Now, run the script from your terminal:

python build_rag.py

You will see the script load the file, build the index, and then correctly answer the questions based *only* on the text provided. Notice how for the dress code question, it honestly says it doesn’t know. That’s a win! Our intern is no longer a liar.

Complete Automation Example

The script above *is* the complete automation walkthrough. It demonstrates the full end-to-end flow: loading a source of truth, indexing it, and using it to answer questions. This simple Python script can now be the core logic for any of the business use cases below. You could wrap it in a simple web framework like Flask to turn it into an API that other applications can call.

Real Business Use Cases

1. HR Department

Problem: HR staff spend hours answering the same questions about benefits, PTO, and company policies.

Solution: Use this RAG script. Feed it the entire employee handbook as a PDF or set of text files. Build a Slack bot that allows employees to ask questions and get instant, accurate answers 24/7.

2. Technical Support Team

Problem: Junior support agents don’t know the answers to complex technical questions and have to escalate tickets, slowing down response times.

Solution: Create a RAG system using all your technical documentation and past support tickets as the knowledge base. When a new ticket comes in, the system can provide the agent with a perfectly drafted, factually correct answer and links to the source documents.

3. Sales Team

Problem: A salesperson is on a call and needs to quickly answer a specific question about a product’s security compliance or integration features.

Solution: Build a RAG-powered chatbot in their CRM or sales tool. The knowledge base is all the product one-pagers, security whitepapers, and case studies. The salesperson can ask, “What SOC 2 compliance do we have?” and get an instant, reliable answer.

4. Financial Analyst

Problem: An analyst needs to compare the financial performance of a company across several years, which requires digging through multiple 100+ page annual reports (10-K filings).

Solution: Load all the 10-K PDFs into a RAG system. The analyst can then ask questions like, “What was the company’s revenue growth in 2021 vs 2022?” and get a synthesized answer with sources cited in seconds.

5. E-commerce Store Owner

Problem: Customers constantly ask nuanced questions about product materials, dimensions, or compatibility that aren’t on the main product page.

Solution: Feed the RAG system the detailed manufacturer spec sheets for every product. The website chatbot can now answer highly specific questions like, “Is this camera lens compatible with a Canon R5 body?” accurately.

Common Mistakes & Gotchas

Poor Document Splitting (‘Chunking’): If you split a document right in the middle of an important sentence, you lose context. The `CharacterTextSplitter` is basic; more advanced strategies exist to split by paragraphs or sections.
Forgetting to Update the Index: The RAG system only knows what you’ve given it. If your company policy changes, you MUST re-run the script to rebuild the vector store. It’s not automatic. A common production setup is to rebuild the index nightly.
Using the Wrong Embedding Model: The quality of your “retrieval” step depends entirely on the embedding model. OpenAI’s models are great but not free. There are open-source alternatives, but they may yield worse search results.
Not Managing Context: Trying to stuff too many retrieved documents into the final prompt can confuse the AI. It’s often better to retrieve the top 3-5 most relevant chunks rather than the top 20.

How This Fits Into a Bigger Automation System

RAG is rarely a standalone tool; it’s the “long-term memory” module for a larger system. It’s the library your other agents check books out from.

Voice Agents: A customer calls your support line. Their speech is converted to text, that text becomes a query for your RAG system, the RAG system finds the right answer in your knowledge base, and an LLM (running on a fast engine like Groq!) synthesizes a friendly response to be spoken back to the customer.
Email Automation: An email comes into `support@mycompany.com`. A tool like Make.com or Zapier triggers a workflow, sends the email body to your RAG API, and the RAG system drafts a reply that a human agent can approve with one click.
Multi-Agent Systems: You can have a “Router” agent that decides if a question is general knowledge (send to a normal LLM) or company-specific (send to your RAG agent). This creates more efficient and cheaper workflows.

What to Learn Next

You’ve done something incredible. You’ve given your AI a brain, and you’ve given that brain a library. It can now learn from your private files and act as a true company expert. This is one of the most commercially valuable skills in AI today.

But our library is still pretty basic. It only contains a single text file. What about PDFs? Websites? Google Docs? What if we want our knowledge base to be automatically updated whenever we publish a new blog post?

In the next lesson, we’re upgrading our librarian. We’ll teach our RAG system how to ingest multiple file types and even how to browse the web to keep its knowledge base constantly up-to-date. We’re moving from a static library to a living, breathing research department.

You’ve mastered the ‘what.’ Next, we master the ‘where.’ Don’t miss it.

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“seo_tags”: “RAG, Retrieval-Augmented Generation, AI Automation, LangChain, OpenAI, Business AI, Custom AI, Knowledge Base”,
“suggested_category”: “AI Automation Courses