Retrieval-Augmented Generation (RAG) chatbots combine the power of large language models (LLMs) with external knowledge bases to deliver accurate, context-aware responses. Unlike traditional chatbots, RAG chatbots retrieve relevant information from a curated dataset before generating answers, reducing AI hallucinations and enabling domain-specific responses.

This guide provides product managers with a step-by-step approach to building a RAG chatbot using n8n, a versatile no-code workflow automation platform, and highlights alternative no-code tools for comparison.

This guide assumes no prior coding experience and focuses on practical steps, tools, and best practices to create a scalable, production-ready chatbot for applications like customer support, internal knowledge bases, or personalized recommendations.

Why Should You Build a RAG Chatbot?

Your team's knowledge is everywhere: PRDs in Google Drive, research in Notion, processes in Confluence, decisions buried in Slack threads. Team members waste hours hunting for information that already exists.

Building a RAG (Retrieval-Augmented Generation) document assistant helps you:

Centralize tribal knowledge into one intelligent interface
Reduce "where is that doc?" questions by 80%
Accelerate onboarding for new team members
Preserve institutional memory when people leave
Ship your first AI product without waiting for engineering

Real Impact: Teams report saving 2-3 hours per person per week on document hunting.

Tools You'll Use (All Free)

n8n - No-code automation platform (free tier: 5K executions/month)
Google Gemini API - Free AI model (60 requests/min, 1M tokens/day)
Google Sheets - Your document index and storage (free)
Google Docs - Source documents (free)
Slack/Teams - Team interface (optional)

Total monthly cost: $0 for teams under 50 people with moderate usage.

Complete Step-by-Step Implementation

Phase 1: Prepare Your Documents

Step 1.1: Document Audit

Open a new Google Sheet and name it "Document Audit"
Create columns: Document Name, Location (URL), Type, Owner, Last Updated, Priority
List all your important documents:
Product Requirements Documents (PRDs)
User research reports
Process documentation
Technical specifications
Meeting notes with decisions
Team guidelines and workflows
Rank each document as High, Medium, or Low priority
Start with your top 20 High priority documents

Step 1.2: Content Preparation

For each priority document, identify the key sections that team members frequently reference
Break long documents into logical sections (aim for 2-4 paragraphs per section)
Ensure each section can stand alone and make sense without reading the full document
Note the document owner for each section (for future updates)

Phase 2: Set Up Your Knowledge Base

Step 2.1: Create the Master Google Sheet

Create a new Google Sheet named "Internal Document Assistant"
Create these tabs:
Document Index (master list)
Content Library (processed sections)
Query Log (track questions)
Settings (configuration)

Step 2.2: Set Up Document Index Tab Create these columns in the Document Index tab:

Column A: Doc ID (DOC001, DOC002, etc.)
Column B: Document Title
Column C: Document Type (PRD, Research, Process, Technical)
Column D: Original URL (link to source)
Column E: Owner Email
Column F: Last Updated Date
Column G: Status (Ready, Processing, Indexed)
Column H: Priority (High, Medium, Low)

Step 2.3: Set Up Content Library Tab Create these columns in the Content Library tab:

Column A: Content ID (DOC001_S01, DOC001_S02, etc.)
Column B: Doc ID (reference to parent document)
Column C: Section Title
Column D: Content Text (the actual content)
Column E: Full Context (Document Title + Section Title)
Column F: Keywords (important terms for searching)
Column G: Embedding Status (Empty, Processing, Complete)

Step 2.4: Populate Your Content

Start with your first priority document
In Document Index, add: DOC001, title, type, URL, owner, date, "Ready", "High"
In Content Library, break this document into sections:
DOC001_S01: Introduction/Overview section
DOC001_S02: Main content section 1
DOC001_S03: Main content section 2, etc.
For each section, fill in:
Section Title (e.g., "User Authentication Requirements")
Content Text (2-4 paragraphs of actual content)
Full Context (e.g., "Mobile App Roadmap Q4: User Authentication Requirements")
Keywords (e.g., "authentication, mobile, biometric, security")
Repeat for your top 10 documents to start

Phase 3: Set Up AI Components

Step 3.1: Get Google Gemini API Key

Go to Google AI Studio
Click "Get API Key" in the top right
Click "Create API Key in new project"
Copy the API key and save it securely (you'll need this for n8n)
Test the API:
In Google AI Studio, try asking "What is artificial intelligence?"
Verify you get a response (confirms your API is working)

Step 3.2: Set Up n8n Account

Go to n8n.cloud
Click "Start for free"
Sign up with your Google account (easier integration)
Verify your email
Choose "Cloud" option (not self-hosted)
Complete the onboarding tour

Step 3.3: Connect Google Sheets to n8n

In n8n, click "Credentials" in the left sidebar
Click "Add Credential"
Search for "Google" and select "Google Sheets API"
Click "Connect my account"
Authorize n8n to access your Google Sheets
Test the connection by creating a simple workflow:
Add "Google Sheets" node
Select your "Internal Document Assistant" sheet
Choose "Document Index" tab
Select "Read" operation
Click "Execute Node" to verify it can read your data

Phase 4: Build Document Processing Workflow

Step 4.1: Create Embedding Generation Workflow

In n8n, click "Add Workflow"
Name it "Document Embedding Generator"

Step 4.2: Add Google Sheets Trigger

Click the "+" button and search "Google Sheets Trigger"
Configure:
Credential: Select your Google Sheets credential
Document: Select "Internal Document Assistant"
Sheet: Select "Content Library"
Trigger On: "Row Added or Updated"
Columns to Watch: Select column D (Content Text)
Click "Listen for Test Event" and add a new row to your Content Library sheet to test

Step 4.3: Add Text Processing

Add a "Code" node after the Google Sheets Trigger
Set Mode to "Run Once for All Items"
In the code field, select "Set via JSON" and enter:

{

"processedText": "={{ $json.Full_Context + '\\\\n\\\\n' + $json.Content_Text + '\\\\n\\\\nKeywords: ' + $json.Keywords }}",

"contentId": "={{ $json.Content_ID }}",

"originalContent": "={{ $json.Content_Text }}"

}

Step 4.4: Add Gemini API for Embeddings

Add "HTTP Request" node
Configure:
Method: POST
URL: https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent?key=YOUR_API_KEY
Replace YOUR_API_KEY with your actual Gemini API key
Headers: Add Content-Type: application/json
Body: JSON, and enter:

{

"model": "models/embedding-001",

"content": {

"parts": [{"text": "{{ $json.processedText }}"}]

}

}

Step 4.5: Store Embeddings Back to Google Sheets

Add "Google Sheets" node
Configure:
Operation: Update
Document: Internal Document Assistant
Sheet: Content Library
Column to Match On: A (Content ID)
Value to Match: {{ $json.contentId }}
Columns to Update:
Column G: {{ $json.embedding.values }}
Save and activate the workflow

Step 4.6: Test the Embedding Workflow

Go to your Google Sheet Content Library tab
Add a test row with sample content
Watch the workflow execute in n8n
Verify that column G gets populated with embedding data
If it works, process your existing content by updating each row (change any character in the Content Text column)

Phase 5: Build Query Processing Workflow

Step 5.1: Create Query Response Workflow

Create a new workflow named "Document Query Processor"

Step 5.2: Add Webhook Trigger

Add "Webhook" node as the trigger
Configure:
HTTP Method: POST
Path: leave empty (n8n will generate a URL)
Copy the webhook URL (you'll need this later)
Click "Listen for Test Event"

Step 5.3: Extract User Question

Add "Set" node after webhook
Configure:
Keep Only Set: Yes
Values to Set:
Name: userQuestion
Value: {{ $json.body.question || $json.body.text || $json.query.question }}

Step 5.4: Generate Query Embedding

Add "HTTP Request" node
Configure exactly like the embedding node in the previous workflow:
Method: POST
URL: https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent?key=YOUR_API_KEY
Headers: Content-Type: application/json
Body:

{

"model": "models/embedding-001",

"content": {

"parts": [{"text": "{{ $json.userQuestion }}"}]

}

}

Step 5.5: Get All Document Content

Add "Google Sheets" node
Configure:
Operation: Read
Document: Internal Document Assistant
Sheet: Content Library
Range: A:G (all columns)
Options: Enable "RAW Data"

Step 5.6: Find Similar Documents

Add "Code" node
Set Mode: "Run Once for All Items"
Use this logic (click "Add Expression" and enter):

// Get the query embedding from the first input

const queryEmbedding = $input.first().json.embedding.values;

// Get all documents from the second input (Google Sheets)

const documents = $input.all().slice(1); // Skip the first item (query)

// Calculate similarity for each document

const results = [];

documents.forEach(doc => {

if (doc.json.Embedding_Status === 'Complete' && doc.json.embedding) {

const docEmbedding = JSON.parse(doc.json.embedding);

// Calculate cosine similarity

let dotProduct = 0;

let normA = 0;

let normB = 0;

for (let i = 0; i < queryEmbedding.length; i++) {

dotProduct += queryEmbedding[i] * docEmbedding[i];

normA += queryEmbedding[i] * queryEmbedding[i];

normB += docEmbedding[i] * docEmbedding[i];

}

const similarity = dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));

if (similarity > 0.6) { // Only include reasonably similar content

results.push({

content: doc.json.Content_Text,

context: doc.json.Full_Context,

similarity: similarity,

sectionTitle: doc.json.Section_Title,

keywords: doc.json.Keywords

});

}

}

});

// Sort by similarity and return top 5

return results

.sort((a, b) => b.similarity - a.similarity)

.slice(0, 5);

Step 5.7: Generate AI Response

Add "HTTP Request" node for Gemini Pro
Configure:
Method: POST
URL: https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=YOUR_API_KEY
Headers: Content-Type: application/json
Body:

{

"contents": [{

"parts": [{

"text": "You are an internal document assistant for our product team. Answer the user's question using ONLY the provided document excerpts. Always include source references.\\n\\nRETRIEVED DOCUMENTS:\\n{{ $json.map(doc => `Section: ${doc.context}\\nContent: ${doc.content}\\n---`).join('\\\\n') }}\\n\\nUSER QUESTION: {{ $('Set').first().json.userQuestion }}\\n\\nPlease provide a helpful answer based on the retrieved documents. Include the section names as sources. If the documents don't contain enough information to answer the question, say so clearly."

}]

}]

}

Step 5.8: Format Final Response

Add final "Set" node
Configure:
Values to Set:
Name: response
Value: {{ $json.candidates[0].content.parts[0].text }}
Name: sourceCount
Value: {{ $('Code').first().json.length }}
Name: timestamp
Value: {{ new Date().toISOString() }}

Step 5.9: Test the Complete Workflow

Save and activate the workflow
Copy the webhook URL
Test using a tool like Postman or curl:
URL: Your webhook URL
Method: POST
Body: {"question": "What is our mobile authentication process?"}
Verify you get a relevant response with sources

Phase 6: Create Team Interface

Option A: Simple Web Form

Create a new Google Doc
Insert this HTML (go to Insert > Drawing > Text box and paste):

<h2>🤖 Internal Document Assistant</h2>

<p>Ask questions about our processes, documents, or decisions:</p>

<button type="submit" style="background: #4285f4; color: white; padding: 10px 20px; border: none; border-radius: 4px; cursor: pointer;">Ask Question</button>

</form>

Replace YOUR_WEBHOOK_URL with your n8n webhook URL.

Option B: Slack Integration

Go to api.slack.com
Click "Create New App" > "From scratch"
Name it "Doc Assistant" and select your workspace
Go to "Slash Commands" in the sidebar
Click "Create New Command":
Command: /ask-docs
Request URL: Your n8n webhook URL
Description: "Ask questions about internal documents"
Go to "Install App" and install to your workspace
Test: Type /ask-docs What's our onboarding process? in Slack

Option C: Microsoft Teams Integration

Download Teams Toolkit for Visual Studio Code (or use the web version)
Create a new bot application
Set the messaging endpoint to your n8n webhook URL
Deploy to your Teams environment
Test by mentioning your bot: @DocAssistant What's our feature review process?

Phase 7: Test with Real Scenarios

Step 7.1: Create Test Cases Test your assistant with these real scenarios:

Document Location: "Where is our Q4 product roadmap?"
Process Questions: "What's our process for feature reviews?"
Historical Context: "Why did we decide against feature X?"
Multi-source: "What user research supports our pricing?"
Follow-up: "Who should I contact about implementing this?"

Step 7.2: Quality Assessment For each test, rate (1-5 scale):

Relevance: Does it address the question?
Accuracy: Is the information correct?
Completeness: Is enough detail provided?
Sources: Are sources properly referenced?

Step 7.3: Optimization Based on test results:

If responses are too generic: Add more specific keywords to your content
If wrong documents are matched: Improve your section titles and context
If responses are incomplete: Break large documents into smaller, focused sections
If sources are missing: Check that your prompt includes source instructions

Phase 8: Deploy and Monitor

Step 8.1: Team Rollout

Start with a small pilot group (3-5 people)
Provide a brief demo showing how to ask questions
Share the interface (web form, Slack command, etc.)
Collect feedback for 1 week
Make improvements based on feedback
Roll out to full team

Step 8.2: Create Usage Dashboard

Add a new tab to your Google Sheet called "Analytics"
Set up your Query Processor workflow to log each query:
Add "Google Sheets" node at the end
Log: Timestamp, Question, Response Quality, User (if available)
Create charts tracking:
Daily question volume
Most common topics
User satisfaction ratings

Step 8.3: Maintenance Schedule

Weekly (15 minutes):

Review failed queries in your logs
Add missing content to address common questions
Update any outdated information

Monthly (1 hour):

Analyze usage patterns
Expand content library based on popular topics
Review and update document ownership
Check for broken links or outdated information

Quarterly (2 hours):

Comprehensive content audit
Team feedback survey
Performance optimization
Consider new features or integrations

Advanced Features to Add Later

Conversation Memory

Modify your Query Processor to include previous questions/answers
Store conversation history in Google Sheets
Include relevant context from recent queries in responses

Role-Based Responses

Add user identification to your webhook
Create role-specific document filters
Personalize responses based on team member's role

Automatic Content Updates

Set up Google Apps Script triggers
Automatically detect when source documents change
Flag content that needs review or re-indexing

Multi-Channel Support

Create separate workflows for different platforms
Customize response formatting for each channel
Maintain consistent experience across interfaces

Measuring Success and ROI

Time Savings Calculation

Before:

Average time to find information: 15 minutes
Team searches per day: 30
Daily time lost: 7.5 hours
Monthly cost (at $50/hour): $7,875

After:

Average response time: 1 minute
Success rate: 80%
Monthly time saved: 25 hours
Monthly value: $1,250

ROI: 1,500%+ (after accounting for setup time)

Key Success Metrics

Usage Rate: Target 70%+ of team using weekly
Resolution Rate: Target 80%+ of questions answered
Satisfaction: Target 4.0+ stars from team feedback
Time Saved: Target 2+ hours per person per week

Troubleshooting Common Issues

1. "No relevant documents found"

Solution:

Check if content has been properly indexed (embeddings generated)
Lower the similarity threshold in your Code node (try 0.5 instead of 0.6)
Add more keywords to your content sections
Ensure document sections are comprehensive and self-contained

2. "Responses are too generic"

Solution:

Improve your AI prompt to be more specific
Add more context to each document section
Include document type and recency in the prompt
Break large documents into more focused sections

3. "Workflow execution fails"

Solution:

Check API key is correct and has quota remaining
Verify Google Sheets permissions are properly set
Test each node individually to isolate the issue
Check n8n execution logs for specific error messages

4. "Responses are slow"

Solution:

Reduce the number of documents being processed
Optimize your similarity calculation
Consider caching common responses
Upgrade to paid n8n plan for faster execution

Success Checklist

Technical Setup Complete

Google Sheets knowledge base created and populated
Gemini API key obtained and tested
n8n workflows built and activated
Document processing workflow tested with sample content
Query processing workflow tested with sample questions
Team interface deployed (web form/Slack/Teams)

Content Quality Verified

Top 20 documents broken into logical sections
Each section has clear context and keywords
All content has been processed and indexed
Test queries return relevant, accurate responses
Sources are properly attributed in responses

Team Adoption Ready

Pilot group identified and trained
Usage instructions documented
Feedback collection process established
Success metrics defined and tracking set up
Maintenance schedule created