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flowchart TD
YOU(["You Write It"])
YOU --> L1
YOU --> L2
YOU --> L3
YOU --> L4
subgraph L1["Layer 1 · Public Channel"]
A1["Technical Articles / Blog"] --> A2["X · LinkedIn"]
A2 --> A3["Brand Awareness · Technical Influence"]
end
subgraph L2["Layer 2 · Dify Association Members Only"]
B1["In-Depth Use Cases / Lessons Learned"] --> B2["Association-Exclusive Publication"]
B2 --> B3["Community Engagement · Ecosystem Development"]
end
subgraph L3["Layer 3 · Partner Training"]
C1["Deployment Docs: Docker / Helm"] --> C3["Independent Partner Delivery"]
C2["App Building Courses · Business Scenarios"] --> C3
end
subgraph L4["Layer 4 · OEM Technical Support Premium"]
D1["Architecture Advice · Consulting Docs"] --> D2["Deep Technical Consulting 1-on-1"]
D2 --> D3["High-Value Contracts · Brand Moat"]
end
style L1 fill:#E5EAFF,stroke:#0033FF,color:#000000
style L2 fill:#E5EAFF,stroke:#0033FF,color:#000000
style L3 fill:#E5EAFF,stroke:#0033FF,color:#000000
style L4 fill:#E5EAFF,stroke:#0033FF,color:#000000
What is Dify: A platform that lets enterprises control their own AI applications without depending on a single AI vendor
Dify Cloud vs Dify Enterprise: SaaS trial vs on-premise private deployment
Which LLMs does Dify support: Integration methods for OpenAI, Claude, Gemini, local models (Ollama), etc.
Dify core concepts explained: What are Chatbot / Agent / Workflow / Knowledge Base, and what scenarios are they suited for
The fundamental difference between Dify Workflow and n8n / Zapier
Building an internal FAQ bot with Dify: End-to-end process from knowledge base upload to conversation testing
Contract review assistant with Dify + Knowledge Base: How to handle PDF documents and set retrieval parameters
Automating daily report generation with Dify Workflow: Multi-node chaining, variable passing, and output format control
Calling external tools with Dify Agent: Using weather queries as an example to demonstrate the full tool definition-to-invocation pipeline
Common AI use cases for Japanese enterprises: Customer service, document processing, internal knowledge retrieval, report generation
What is MCP (Model Context Protocol): Why it standardizes AI tool invocation
The core challenge of RAG: Why knowledge base retrieval results are inaccurate — from chunking strategies to embedding model selection
Prompt engineering pitfalls: The 3 most common writing mistakes made by Japanese enterprises
Limitations of AI Agents: Which tasks are not suited for Agent handling
The necessity of on-premise AI application deployment: Practical considerations around data sovereignty, compliance, and network isolation
Manufacturing equipment troubleshooting bot: Knowledge base layered structure design + retrieval parameter tuning log
Legal contract comparison Workflow: Iteration node for multi-file processing, trigger conditions for human-in-the-loop review nodes
Internal approval (ringi) automatic draft generation: Structured output format design + prompt version comparison
Multilingual customer service Agent: Tool invocation chain design, fallback strategies, conversation memory management
HR onboarding document processing pipeline: Full node configuration from PDF parsing to information extraction to form filling
Knowledge base retrieval returns irrelevant results: How to jointly adjust Top-K, score threshold, and Rerank model
Frequent Workflow node timeouts: Configuration for LLM node timeout parameters, retry mechanisms, and async processing
Inconsistent answers to the same question: Impact of Temperature parameter and prompt structure on output stability
Large file (100MB+ PDF) upload failures: Practical solutions for chunked uploads, preprocessing scripts, and storage configuration
Agent tool invocation stuck in infinite loops: Max iteration count settings + exit condition prompt design logic
Production vs test environment configuration differences: Resource specs, log levels, and backup strategy comparison
Helm Chart deployment values.yaml key parameters explained: Which must be changed and which can stay at defaults
Multi-tenant permission design: How different departments within an enterprise isolate data and share model configurations
License management practices: License expiration monitoring, renewal process, and allocation strategies for multi-instance scenarios
Version upgrade considerations: Data migration, API compatibility, and rollback plans
Docker Compose full deployment walkthrough: Role and dependencies of each service in docker-compose.yaml
Helm Chart full deployment walkthrough: Kubernetes environment requirements, namespace planning, persistent storage configuration
License activation and verification: Key format explanation, activation API, troubleshooting steps for activation failures
Basic operations manual: Service restart order, log locations, common error code reference table
SSO integration configuration: SAML / OIDC protocol selection, IdP configuration parameters, user permission mapping rules
Three knowledge base construction methods and their use cases: Boundaries of configuration-based / pipeline / Web platform import approaches
Prompt design guidelines: Role definition, output format constraints, standard approach for Few-shot examples
Workflow node types and composition patterns: Typical combinations of sequential execution, conditional branching, iteration, and human-in-the-loop
Agent tool definition guidelines: How to write tool description fields so the LLM can invoke them accurately
API integration guide: Published API Endpoint structure, authentication methods, and request/response format documentation
Deployment acceptance criteria: Which services must be healthy, which APIs must pass, License status confirmation items
Basic functionality verification items: End-to-end test cases from knowledge base upload to retrieval to conversation
Customer handover document template: Environment information record, admin account handover form, operations contact details
Common customer Q&A library: The 20 most frequently encountered questions and standard answers during partner delivery
Upgrade and renewal reminder mechanism: Version update notification channels, customer communication scripts before License expiration
Why Dify chose on-premise over pure SaaS: Data sovereignty, enterprise compliance, and the unique aspects of the Japanese market
Dify’s Provider abstraction layer design: Why it can integrate dozens of LLMs simultaneously without coupling
Knowledge Base retrieval architecture: Why the three layers of vector search + full-text search + Rerank are indispensable
Workflow node design philosophy: Why the “human-in-the-loop” node exists and where its boundaries lie
Dify’s multi-tenant model: Workspace isolation granularity, permission inheritance relationships, and design trade-offs
Layered architecture for enterprise AI applications: Responsibilities of the infrastructure layer / platform layer / application layer / user layer
Model selection recommendation framework: Recommending different model combinations by task type (generation / retrieval / classification / multimodal)
Knowledge base scaling design: Partitioning strategies, index maintenance, and retrieval performance guarantees when document volume exceeds 100K
High-availability deployment architecture: Reference configurations for multi-replica, load balancing, database primary-replica, and disaster recovery
AI application security boundary design: A complete solution for input filtering, output review, and tool invocation permission control
Enterprise AI implementation roadmap template: Milestone definitions for pilot phase, scale-up phase, and autonomous operation phase
AI ROI assessment framework: How to quantify the actual value of AI applications in efficiency gains and cost savings
Enterprise AI committee setup recommendations: Role assignments for who owns AI strategy, technology, and compliance
Data governance and AI compliance: Constraints of Japan’s Act on the Protection of Personal Information (amended APPI) on AI applications
Organizational capability building path: Boundaries and transition strategies between internal AI talent development and reliance on partners