After this lesson, you will be able to: Use Dify's prompt editor and workflow canvas to build multi-step AI pipelines, manage prompt versions, and integrate API tools.
Dify is the prompt-engineer-friendly low-code platform. Its prompt IDE has version control, A/B testing, and inline evaluation. Its workflow canvas pieces prompts together into multi-step apps. This lesson builds a content generation pipeline with conditional branching.
Apps, chatbot, text-completer, agent, workflow. Prompt IDE, versioned prompts, side-by-side A/B testing, model comparison. Workflow canvas, chain LLM calls, code blocks, HTTP requests, IF/ELSE branches into a single app.
1. Sign up at dify.ai (free tier; or self-host via Docker, `git clone github.com/langgenius/dify && docker compose up`).
2. Add a model provider, paste your OpenAI / Anthropic / etc. API key in Settings.
3. Click 'Create from blank app'. Pick 'Workflow'.
1. Start node: variable 'topic' (string input).
2. Add LLM node: 'Generate 3 angles for a blog post on {{topic}}, return as JSON array.' Output as 'angles'.
3. Add Code (Python) node: parse JSON, pick the angle with most novelty (or just first one for v1).
4. Add IF/ELSE: if length(angles) > 0 → continue; else → end with error.
5. Add LLM node: 'Write a 500-word article on the angle: {{chosen_angle}}.' Output as 'article'.
6. End node: return 'article'.
Tools, built-in (Google search, DALL-E, Wolfram) and custom (OpenAPI specs become tools). Knowledge bases, upload docs, set chunking, attach to apps as RAG context with one click. Both are Settings-level, reusable across apps.
Sign in and purchase access to unlock this lesson.