AI TRAINING
Dify & Flowise for Visual LLM App Building
Build, deploy, and maintain production-ready LLM apps without writing backend code.
What it covers
This hands-on programme teaches non-engineer builders and IT teams how to use Dify and Flowise to design RAG pipelines, multi-step agents, and API-connected workflows through visual interfaces. Participants learn to self-host both platforms, connect external data sources, and evaluate output quality. The format combines guided walkthroughs, live build sessions, and a capstone project where each team ships a working internal tool. The course also covers when visual builders hit their limits and how to hand off cleanly to engineers.
What you'll be able to do
- Stand up a self-hosted Dify or Flowise instance on a cloud VM using Docker within two hours
- Build a working RAG pipeline that retrieves from a custom document store and returns grounded answers
- Design and test a multi-step agent with conditional branching and at least one external tool call
- Evaluate LLM output quality using built-in scoring and tracing tools inside Dify
- Identify the architectural threshold at which a visual workflow should be rewritten in code and document the handoff requirements
Topics covered
- Dify platform overview: projects, datasets, and prompt orchestration
- Flowise canvas: chaining LLM nodes, memory, and tools
- Building RAG pipelines with custom document stores
- Designing multi-step agents with tool-use and decision branches
- Self-hosting Dify and Flowise on cloud VMs or Docker
- Connecting external APIs, webhooks, and databases as data sources
- Evaluating and debugging LLM outputs within visual workflows
- When to migrate from visual builders to code-first frameworks
Delivery
Delivered as a blended programme over two to three days, either fully remote via video call with shared cloud sandboxes, or on-site with participant laptops. Each session is roughly 60% hands-on build time and 40% guided instruction. Participants receive pre-provisioned Dify and Flowise cloud environments, reference architecture diagrams, and a library of reusable workflow templates. A 30-day async Slack channel is included for post-training troubleshooting.
What makes it work
- Start with a real internal use case the team already needs, so the capstone project has immediate business value
- Assign a technical co-pilot (even a part-time developer) who can own the self-hosting infrastructure
- Establish a review cadence for prompt and workflow changes before they reach end-users
- Document the graduation criteria — agree upfront on what triggers a rewrite in LangChain or similar
Common mistakes
- Treating visual builders as a permanent solution for complex agent logic that later becomes unmaintainable without engineering support
- Skipping self-hosting setup and relying entirely on cloud-managed tiers, then hitting data-privacy or cost limits in production
- Uploading raw unstructured documents without chunking strategy, resulting in poor RAG retrieval quality
- Ignoring observability — not instrumenting tracing or logging, so debugging failures in production is blind
When NOT to take this
This training is not the right fit for a team that already has full-stack engineers and needs to build a high-throughput, multi-tenant LLM service — they should go straight to a code-first framework like LangChain or LlamaIndex rather than learn a visual abstraction they will outgrow immediately.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.