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No-Code AI Development with Flowise · LearnSpace
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courseraПрограммирование

No-Code AI Development with Flowise

Курс от Edureka
Начальный≈ 5.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

No-code AI development is becoming one of the fastest ways to build intelligent applications without writing complex code. In this hands-on course, you’ll learn how to use Flowise, a visual no-code AI workflow platform that helps you build LLM-powered workflows, prompt chains, RAG systems, tool-using agents, and deployable AI applications through an intuitive drag-and-drop interface. You’ll begin by understanding the foundations of Agentic AI and no-code AI development, including how AI workflow platforms work, how nodes connect, and how data flows through a visual workflow. Then, you’ll move through practical exercises from setting up Flowise and configuring model providers to creating prompt templates, generating structured JSON outputs, building multi-step chains, designing RAG workflows, connecting tools, adding guardrails, and deploying complete AI workflows. By the end of this course, you will be able to: -Define the core concepts of Agentic AI, no-code AI development, Flowise workflows, nodes, chains, tools, and data flow. -Set up Flowise, configure model providers, connect LLMs, manage API keys, and understand token usage and AI API costs. -Design reliable prompts, use variables, generate structured JSON outputs, and build multi-step LLM processing chains. -Build document-grounded RAG workflows using document loading, chunking, embeddings, vector stores, metadata, and retrieval optimization. -Create tool-using agents, handle failed tool calls, apply guardrails, evaluate workflow performance, and deploy AI workflows responsibly. This course is designed for beginners, software developers, AI enthusiasts, automation professionals, business analysts, product teams, and learners who want to build AI-powered workflows without writing full application code. If you are new to Flowise, no-code AI tools, or Agentic AI workflows, this course provides a practical starting point. Learners should have basic familiarity with AI and an interest in building workflow-based applications. Prior coding experience is helpful but not required. Familiarity with APIs, prompts, documents, and basic web tools will make the hands-on exercises easier to follow. Enroll now and learn how to design, build, test, evaluate, and deploy AI workflows with Flowise. Start with the fundamentals, practice with real no-code AI workflows, and build confidence using visual AI development as part of modern automation and application-building workflows.

Навыки, которые вы освоите

AI WorkflowsEmbeddingsJSONPrompt EngineeringAgentic WorkflowsRetrieval-Augmented GenerationAgentic systemsResponsible AIIntegration TestingGenerative AI AgentsKnowledge TransferArtificial Intelligence and Machine Learning (AI/ML)Generative AIArtificial IntelligenceAI EnablementVector DatabasesApplication Programming Interface (API)AI SecurityAI PersonalizationPrompt Patterns

Программа курса

3 модулей · 44 учебных материалов

01Flowise Foundations and AI Workflow Development14 материалов
Course IntroductionВидеоCourse Overview : No Code AI Development With Flowise ЧтениеIntroduction to Agentic AI and No-Code AI DevelopmentВидеоHow No-Code AI Workflow Platforms WorkВидео

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Edureka

Преподаватель курса

No-Code AI Development with Flowise
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 5.5 ч

3 модулей

Язык: Английский

Часть программы вашего университета
Hands-On: Understanding Flowise Workspace and Visual BuilderВидео
Hands-On: Setting Up Flowise for Your First ProjectВидео
Flowise Setup and Model Provider ChecklistЧтение
Hands-On: Configuring Model Providers and Connecting Your First LLMВидео
AI API Costs, Tokens, and Budgeting in FlowiseЧтение
Flowise Setup and API Key Задание
Anatomy of a Flowise Workflow: Nodes, Chains, and Data FlowВидео
Hands-On: Build, Test, and Share Your First Text Processing WorkflowВидео
Key Flowise Components and TerminologyЧтение
Knowledge Check: Flowise Foundations and First AI WorkflowЗадание
02Prompt Engineering Structured Outputs and RAG Workflows15 материалов
Designing Prompts for Reliable AI Workflow OutputsВидеоHands-On: Creating Prompt Templates with VariablesВидеоHands-On: Producing Structured JSON Outputs in FlowiseВидеоPrompt Engineering Framework Cheat SheetЧтениеHands-On: Building a Multi-Step LLM Processing ChainВидеоHands-On: Testing and Debugging Chain OutputsВидеоPrompts, JSON Outputs, and ChainsЗаданиеWhy RAG Matters for Document-Grounded AI WorkflowsВидеоHands-On: Loading and Chunking Documents in FlowiseВидеоData Preparation Guide for Flowise RAG SystemsЧтениеHands-On: Creating a RAG Workflow with Embeddings and Vector StoresВидеоHands-On: Improving Retrieval with Top-K, Chunk Size, and MetadataВидеоRetrieval Quality and RAG Troubleshooting ChecklistЧтениеAnalyzing Real-World Agentic AI Workflows and Flowise Deployment PracticesDIALOGUEKnowledge Check: Prompt Engineering Structured Outputs and RAG WorkflowsЗадание
03Flowise Agents Tool Integration Evaluation and Deployment15 материалов
From Chains to Agents: Reasoning and Tool UseВидеоHands-On: Building Your First Tool-Using AgentВидеоHands-On: Connecting Calculator, Search, and Custom ToolsВидеоAgent Tools and Workflow Design GuideЧтениеHands-On: Handling Agent Errors, Fallbacks, and Failed Tool CallsВидеоAgent Concepts and Tool Use CheckЗаданиеHands-On: Controlling Agent Behavior with GuardrailВидеоEvaluating AI Workflows: Accuracy, Cost, Latency, and SafetyВидеоAI Security and Prompt Injection Mitigation GuideЧтениеHands-On: Deploying, Monitoring, and Iterating a Flowise WorkflowВидеоPre-Launch Optimization and Workflow Monitoring ChecklistЧтениеJunior No-Code AI Workflow Developer InterviewDIALOGUEPractice Project: AI-Powered Knowledge Assistant with FlowiseЧтениеFinal Course Assessment: No Code AI Development with FlowiseЗаданиеCourse SummaryВидео