Dify.ai Learning Platform - Master AI Development Without Coding

What is Dify.ai?

Dify is an open-source LLMOps platform that combines Backend-as-a-Service and LLMOps to streamline the development of generative AI solutions, making it accessible to both developers and non-technical innovators.

Visual AI Orchestration

Design AI workflows visually with drag-and-drop interface. No coding required.

Knowledge Base & RAG

Build smart chatbots that can access and retrieve information from your documents.

AI Agents

Create autonomous AI agents that can use tools and make decisions independently.

Prompt Engineering

Master the art of prompt engineering with built-in IDE and testing tools.

Tool Integration

Connect to external APIs and tools without writing complex integration code.

Monitoring & Analytics

Track performance, costs, and usage with comprehensive analytics dashboard.

Your Learning Journey

Follow our structured path from beginner to AI application expert

1

Foundation

Learn the basics of AI, LLMs, and the Dify platform

2

First Projects

Build your first chatbot and AI agent with guided tutorials

3

Advanced Workflows

Master complex workflows, API integrations, and optimization

4

Production & Deployment

Deploy, monitor, and scale your AI applications

Module 1: Foundation

Build your understanding of AI concepts and get started with Dify

Lesson 1.1: Understanding AI & LLMs

What are Large Language Models?

Large Language Models (LLMs) are artificial intelligence systems trained on vast amounts of text data. They can understand and generate human-like text, making them perfect for building conversational AI applications.

Key Concept: Think of LLMs as extremely knowledgeable assistants that can help with almost any text-related task - from answering questions to writing content.

Understanding Context Windows

A context window is the amount of text an LLM can "see" at once. It's like the model's working memory - everything it considers when generating a response.

Context Window = Input Prompt + Previous Conversation + Output Space

Example:
- GPT-4: 8,000 tokens (~6,000 words)
- GPT-4 Turbo: 128,000 tokens (~96,000 words)
                        

What are Embeddings?

Embeddings convert text into numerical vectors that capture meaning. Similar concepts have similar vectors, enabling semantic search and knowledge retrieval.

Real-world analogy: Like a GPS converting addresses into coordinates, embeddings convert words into mathematical coordinates that preserve meaning relationships.

Lesson 1.2: Getting Started with Dify

Setting Up Your Account

Visit dify.ai and create a free account
Verify your email address
Complete the onboarding tutorial
Explore the dashboard interface

Configuring API Keys

To use AI models, you'll need API keys from model providers:

OpenAI: Get your API key from platform.openai.com

Anthropic: Get Claude API key from console.anthropic.com

Free Tier: Dify provides limited free usage for testing

Dashboard Overview

Studio

Build applications

Knowledge

Manage data

Tools

Add integrations

Logs

Monitor usage

Module 2: Your First AI Projects

Build real applications with step-by-step guidance

1

Project 1: Smart Customer Service Bot

🎯 Learning Objectives

Understand Knowledge Bases and RAG
Learn about context windows and hallucinations
Master semantic search vs keyword search
Build your first chatbot application

📋 What You'll Build

A customer service chatbot that can answer questions about your business using uploaded documents. When the bot doesn't know something, it will automatically search Google for additional information.

🔧 Step-by-Step Process

Step 1: Create Knowledge Base

• Navigate to Knowledge → Create Knowledge
• Upload your documents (PDF, TXT, etc.)
• Configure chunking and embedding settings
• Test retrieval with sample queries

Step 2: Build the Chatbot

• Create new Chatflow application
• Add Knowledge Retrieval node
• Connect to your knowledge base
• Configure LLM with appropriate prompt

Step 3: Add Fallback Logic

• Add Condition node to check confidence
• Connect Google Search tool as fallback
• Format responses appropriately
• Test with various question types

💡 Sample Prompt Template

You are a helpful customer service assistant for [Company Name]. 

Your primary job is to answer customer questions using the knowledge base provided. When answering:

1. Always be polite and professional
2. Use the knowledge base information when available
3. If you're not confident about an answer, say so clearly
4. Offer to search for additional information if needed

Context from knowledge base: {{knowledge_base_context}}
Customer question: {{query}}

Please provide a helpful and accurate response.
                    

2

Project 2: AI Travel Consultant Agent

🎯 Learning Objectives

Understand AI Agents and autonomous behavior
Learn Chain-of-Thought reasoning
Master prompt engineering techniques
Integrate external tools and APIs

🌍 What You'll Build

An intelligent travel consultant that can research destinations, find hotels, suggest restaurants, and create detailed itineraries using multiple external tools and data sources.

🛠️ Tools & Integrations

Wikipedia Search

Google Search

Web Scraping

Maps API

🚀 Agent Configuration

Agent Prompt Structure

# Role
You are an expert travel consultant AI agent.

# Skills
- Destination research and recommendations
- Hotel and accommodation booking assistance  
- Restaurant and activity suggestions
- Itinerary planning and optimization
- Budget estimation and planning

# Workflow
1. Understand the user's travel preferences
2. Research destination information
3. Find suitable accommodations
4. Suggest activities and dining
5. Create detailed itinerary
6. Provide helpful travel tips

# Constraints
- Always verify information from multiple sources
- Consider budget constraints mentioned by user
- Suggest alternatives when options are limited
- Be culturally sensitive in recommendations
                            

Example Interaction Flow

USER

"Plan a 3-day trip to Tokyo for $1500"

AGENT

"Let me research Tokyo destinations and find budget-friendly options..."

TOOLS

Wikipedia search → Google search → Hotel booking APIs

AGENT

"Here's your customized Tokyo itinerary with budget breakdown..."

Module 3: Advanced Workflows

Master complex AI workflows and integrations

Workflow Patterns

Prompt Chains

Break complex tasks into sequential steps, using output from one step as input for the next.

Example: Recipe Generator → Check Ingredients → Suggest Alternatives → Format Output

Routing & Classification

Direct user input to specialized workflows based on intent classification.

Example: Support Query → Classify (Technical/Billing/General) → Route to Specialist Agent

Parallelization

Run multiple LLMs simultaneously for diverse outputs or independent subtasks.

Example: Content Ideas → [Creative Writer + Technical Writer + SEO Specialist] → Combine Results

Orchestrator-Workers

A coordinator distributes tasks to specialized workers when subtasks are unpredictable.

Example: Research Project → Orchestrator → [Data Collector + Analyst + Writer] → Final Report

API Integration & External Tools

Setting Up API Connections

OpenAPI/Swagger Integration

Import API specifications to automatically create tool nodes

Custom API Endpoints

Connect to your internal APIs with custom headers and authentication

Pre-built Integrations

Use ready-made connectors for popular services

Popular Tool Integrations

Google APIs

Social Media

Analytics

Finance APIs

Email Services

Databases

Advanced Project: Multi-Step E-commerce Assistant

Product Discovery

Classify user intent and search product catalog

Purchase Processing

Handle cart operations and payment processing

Customer Service

Provide support and handle inquiries

Workflow Configuration

Start → Question Classifier → [Product Search | Purchase Intent | Support Query]
    ↓                    ↓                     ↓                    ↓
Product Search:      Purchase Intent:      Support Query:
- Search API         - Validate Cart       - Knowledge Base
- Filter Results     - Process Payment     - Escalate if needed
- Show Options       - Send Confirmation   - Log interaction
        

Module 4: Production & Deployment

Deploy, monitor, and scale your AI applications

Deployment Options

Cloud Hosting (Recommended for Beginners)

One-click publishing
Automatic scaling
Built-in analytics
SSL certificates included

API Integration

Embed AI capabilities into existing applications:

# RESTful API Example
curl -X POST "https://api.dify.ai/v1/chat-messages" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": {},
    "query": "Hello, how can you help me?",
    "user": "user-123"
  }'
                            

Self-Hosted Deployment

Full control with Docker deployment:

# Clone repository
git clone https://github.com/langgenius/dify.git

# Navigate to docker directory  
cd dify/docker

# Start all services
docker compose up -d

# Access at http://localhost
                            

Monitoring & Analytics

Key Metrics to Track

Active Users

Daily/Monthly usage

Conversations

Total interactions

Response Time

Average latency

Token Usage

Cost tracking

Performance Optimization

Model Selection

Choose appropriate models for your use case

Prompt Optimization

Reduce token usage with efficient prompts

Memory Management

Optimize conversation history storage

Scaling Strategies

Start with cloud hosting for rapid deployment
Monitor usage patterns and performance metrics
Optimize prompts and model selection based on data
Consider self-hosting for higher volumes
Implement caching and rate limiting
Set up automated monitoring and alerts

Resources & Next Steps

Continue your journey with these valuable resources

Official Documentation

Community Resources

Advanced Learning

Quick Reference Cheat Sheet

Common Workflow Nodes

Start Node Initialize workflow

LLM Node Process with AI model

Knowledge Retrieval Search knowledge base

Code Node Execute custom logic

Condition Node Branch workflow logic

Best Practices

Test prompts in the Prompt IDE before deployment
Use descriptive names for variables and nodes
Monitor token usage and costs regularly
Implement error handling and fallback logic
Start simple and iterate based on user feedback
Use knowledge bases to reduce hallucinations

Start Building Your AI Applications Today

You now have all the knowledge needed to create powerful AI applications with Dify. From simple chatbots to complex multi-agent systems, the possibilities are endless.

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Keep Learning

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