Do I need machine learning or data science experience for this course?

No. This course focuses on practical AI workflow building using tools like LangFlow, n8n, and LLM APIs — not on training models from scratch. Programming experience and basic API familiarity are sufficient.

What tools and frameworks are covered?

The course covers LangFlow, n8n, LangChain, LlamaIndex, LangSmith, DeepEval, Promptfoo, Ollama, and multi-agent patterns including MCP and A2A. You'll work with both hosted and local LLMs.

Will I build something real during the course?

Yes. By the end of the course, you'll have created working AI bots, RAG pipelines, and automation workflows. The final lab involves designing and presenting your own AI workflow or assistant.

Can I use my company's own data and use cases?

Absolutely. The exercises are designed to be adaptable. During the final lab, you're encouraged to work on a use case relevant to your organization.

Is the course delivered live or is it pre-recorded?

The course is delivered live and online. You can interact with the instructor in real-time, ask questions via chat or audio, and participate in hands-on exercises. Video recordings are provided after the course for review.

Will I receive a certificate after completing the course?

Yes, all participants receive a shareable online certificate of attendance. You can also take an optional 1-hour exam, with your score printed on the certificate.

Popular workshop online

AI Workflows: Practical Systems with LLMs

Practical AI Course

Level: advanced

Duration: 2 days

Language: English

This 2-day hands-on course explores how to build intelligent workflows and assistants by combining Large Language Models with structured logic and automation platforms.

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About the AI Workflows: Practical Systems with LLMs Course

This 2-day workshop introduces participants to the tooling and design principles needed to build AI-powered workflows and systems. We'll cover LangFlow, n8n, LLM prompt engineering, fine-tuning, RAG pipelines, agent orchestration, and the trade-offs between hosted and local models. By the end of the course, you will have created working AI bots and automation tailored to your context.

What You'll Learn in This workshop

Understand how to build AI systems with LLMs and workflow tools
Learn to evaluate and control responses using real-world automation
Gain practical skills to deploy internal assistants and bots
Design multi-agent systems and RAG pipelines for real-world use cases

AI Workflows: Practical Systems with LLMs Outline

1. Day 1 — Foundations: From Prompts to Workflows

• Understanding the AI Workflow Landscape: what LLMs can and can’t do, common workflow types, and modern tooling overview (LangFlow, n8n, LangChain)
• Creating your first LLM-powered automation: using n8n and LangFlow to build workflows and connect logic
• Prompt Engineering Essentials: few-shot prompting, roles, output formatting, common mistakes and fixes
• RAG in Practice: building a retrieval-augmented assistant using LlamaIndex or LangChain and a vector database
• Modular AI workflows: breaking down tasks into steps using multi-turn logic and orchestration patterns

2. Day 2 — Agents, Autonomy, and Evaluation

• Multi-Agent Systems Overview: MCP, A2A patterns, AutoGen-style orchestration, role assignment and structured dialogue
• Tool-Augmented Agents: implementing agents that call external tools, APIs, and internal services dynamically
• Evaluation and Testing: LangSmith, DeepEval, Promptfoo, designing test cases and using LLM-as-a-judge
• Local vs Cloud Models: performance, privacy, and cost trade-offs, including Ollama and fallback strategies
• Final Lab: design and present your own AI workflow or assistant using techniques and tools covered in the course