Building and Deploying RAG Systems with LangChain and No-Code Tools

Deepen your expertise in Retrieval-Augmented Generation by building, testing and comparing real-world RAG systems across two intensive hands

Training content

This advanced, practice-oriented training focuses on the implementation, debugging and evaluation of RAG pipelines. Participants will build a complete RAG system using code-based frameworks and explore no-code/low-code alternatives, enabling informed architectural choices in real deployment contexts.

This training is part of a three-day RAG course series. Days 2 and 3 form a consecutive technical module and cannot be attended separately. While Day 1 focuses on conceptual foundations, Days 2 and 3 are dedicated to hands-on implementation and require a technical background. Participants registered for the full 3-day program receive a €50 discount. To register for the complete program, register on day 1 and add-on days 2 and 3 in the checkout process.

This training will be conducted in English.

Program

DAY 2

Morning – Building RAG with LangChain

• Introduction to LangChain concepts (chains, retrievers, prompts)
• Assembling a complete RAG pipeline step by step
• Using multiple LLMs within a single workflow
• Managing prompts and retrieval strategies

Afternoon – Monitoring, Debugging and Optimisation

• Tracing and monitoring with LangSmith
• Diagnosing retrieval and generation errors
• Analysing latency, costs and response quality
• Iterating on chunking, retrieval and prompting strategies

DAY 3

Morning – Visual and No-code RAG Solutions

• Building RAG pipelines with Langflow / Flowise
• Visual configuration of loaders, chunking, embeddings and retrievers
• Local execution and cloud-based deployment considerations

Afternoon – Cloud-based RAG & Comparative Workshop

• Creating RAG-based agents with Microsoft Copilot Studio
• Connecting to structured and unstructured knowledge sources
• Code-based vs no-code architectures
• Strengths, limitations and suitable use cases
• Trade-offs in flexibility, maintainability and scalability

Target audience

Developers (backend, full-stack)
Data, ML and AI engineers
Technical consultants and solution architects
IT and digital teams responsible for AI implementation