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AI

Retrieval Augmented Generation (RAG) with LangChain

The “Retrieval Augmented Generation (RAG) with LangChain” training is an intensive 2–3-day hands-on workshop that introduces participants to building practical RAG systems — advanced applications that combine information retrieval with generative language models.

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Who it's for

Ideal for teams that…

1 Programmers and AI engineers who want to build RAG applications
2 Data scientists and NLP specialists integrating LLMs with their own data sources
3 Analysts and developers interested in practical automation of knowledge access
4 System architects exploring modern approaches to combining retrieval and generation
Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

✓

Prepare and index data for RAG systems in a complete workflow

✓

Design and implement Retrieval-Augmented Generation pipelines with LangChain

✓

Master advanced chunking, embedding, and retrieval techniques

✓

Build efficient Q&A systems and chatbots powered by custom data

✓

Optimize prompting and manage conversational context

✓

Create scalable and secure RAG solutions ready for production deployment

Program · 6 modules

What we actually do

M01
Module 1: Fundamentals of Retrieval Augmented Generation
  • · The idea and advantages of RAG compared to standard text generation
  • · RAG architecture: indexing, retrieval, and generation
  • · Components and data flow in RAG systems
  • · Introduction to the LangChain library and its RAG-supporting modules
M02
Module 2: Data Preparation and Indexing
  • · Document loading and chunking techniques
  • · Creating and applying embeddings — representing text in vector space
  • · Implementing VectorStores for data storage and vector-based search
  • · Integrating data from various sources (Python code, HTML, PDF, etc.)
  • · Workshop: indexing your own documents and testing retrieval
M03
Module 3: Constructing a Retrieval + Generation Pipeline in LangChain
  • · Implementing retrievers with different parameters (dense/sparse, BM25)
  • · Building retriever and LLM components
  • · Combining retrieval with LLM prompting into a full RAG pipeline
  • · Implementing a simple Q&A system with RAG
  • · Using LangGraph for orchestration and application state management
M04
Module 4: Pipeline Optimization and Personalization
  • · Prompt tuning, context management, and token limit handling
  • · Adding conversational history and user context
  • · Techniques for minimizing hallucinations and ensuring consistency
  • · Workshop: tuning the pipeline and adding conditional logic
M05
Module 5: Deploying RAG Applications in Production
  • · Building APIs and frontends for RAG applications (Flask/FastAPI)
  • · Working with multimodal data (PDFs, images, etc.) in RAG
  • · Security, monitoring, and scaling RAG systems
  • · Safeguards against hallucinations and bias — validation and control
  • · Practical project: implementing and testing a full RAG application in a chosen scenario
M06
Module 6: Supporting Tools and the Future of RAG with LangChain
  • · Integrating LangGraph and LangSmith for debugging and workflow auditing
  • · Automation and human-in-the-loop approaches for controlled generation
  • · Trends and emerging opportunities for RAG in the AI ecosystem
  • · Wrap-up, consultations, and personal development roadmap
Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.
How we work

From brief to retro in 30 days.

01

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

02

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

03

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

04

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

✓Quote within 48h of the brief
✓First session within 30 days
✓Pilot before the full decision
✓VAT invoice, payment in instalments possible

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