FORMATION IA
Dify & Flowise pour Applications LLM Visuelles
Construisez, déployez et maintenez des applications LLM prêtes pour la production sans écrire de code backend.
Ce qu'elle couvre
Ce programme pratique enseigne aux équipes IT et aux builders non-développeurs comment utiliser Dify et Flowise pour concevoir des pipelines RAG, des agents multi-étapes et des workflows connectés à des API via des interfaces visuelles. Les participants apprennent à auto-héberger les deux plateformes, à connecter des sources de données externes et à évaluer la qualité des sorties. Le format combine des walkthroughs guidés, des sessions de construction en direct et un projet capstone où chaque équipe livre un outil interne fonctionnel. Le cours aborde également les limites des outils visuels et les conditions du passage au code.
À l'issue, vous saurez
- 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
Sujets abordés
- 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
Modalité
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.
Ce qui fait que ça marche
- 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
Erreurs fréquentes
- 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
Quand NE PAS suivre cette formation
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.
Fournisseurs à considérer
Sources
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