Workshop: Building a RAG system
Mentor - Natalia Manakova, Senior Data Scientist, AI consultant at SoftServe, PhD
Most AI tools work well with general knowledge, but have difficulty working with real documents: internal knowledge bases, PDFs, notes, customer data, etc. This is where RAG (Retrieval-Augmented Generation) comes in — an approach that allows you to combine searching your data with LLM capabilities.
RAG systems are also actively used in products: from chatbots to corporate knowledge systems, which makes these skills relevant for the modern market.
In this workshop, you will step by step assemble your own RAG system that will answer questions based on your documents and add links to sources.
- In the process, you will understand the key components of modern AI solutions:
- how semantic search works
- what are embeddings and how are they used
- how vector databases work
- how to combine retrieval and generation into one system
The workshop format combines a brief theory, live coding, and practical examples. You will not only understand how RAG works, but you will also be able to independently implement the basic version and adapt it to your tasks.
As a result, you will receive an example of a ready-made system and a clear understanding of how to apply these approaches in practice to really save time and increase team efficiency.
Audience
- Data Scientists / ML Engineers (classic ML → GenAI)
Experienced in classic ML who want to move to GenAI and gain hands-on experience with LLM, embeddings, and RAG. - Data / Business Analysts
Analysts who work with large amounts of text data and want to automate information search and analysis using AI. - AI / GenAI enthusiasts (with basic knowledge)
Experts familiar with ChatGPT or OpenAI API who want to move from prompt experiments to building full-fledged AI systems. - Tech Leads / Architects
Technical leaders who evaluate and implement AI solutions and want to gain a deeper understanding of the RAG architecture and its application in production.
Format:
Date and time: May 19&21, online, start at 18:30 (Kyiv time, GMT+2)
Duration of each session: ~2.5 hours
Platform: Zoom. You will receive a link to the broadcast the day before the workshop, and it will also be available on this landing page.
Recommended toolset and subscriptions:
Event and presentation language: Ukrainian
Program
- Basics: what is RAG and why is it needed
- Architecture: how the RAG system works (retrieval + generation)
- Data: document preparation (PDF / text)
- Preparation data: chunking and embeddings for efficient search
- Infrastructure: vector databases
- Integration: connection to LLM
- Implementation: pipeline construction
- Practice: demonstration and testing of the system
Mentor: Natalia Manakova, Senior Data Scientist, SoftServe
— Has experience in developing AI solutions using LLM and RAG
— The portfolio includes production-ready GenAi/RAG/Agentic systems
— PhD in Applied Mathematics and Advanced Information Technologies
— Speaker at conferences and mentor in WomanWhoCode programs
— Conducts research on AI in the company's internal lab
Event price
Access to online course sessions
Access to course recordings for a year
Presentations
Additional materials from the speaker
Access to a chat where the speaker will answer questions
Participant certificate
Free month in Fwdays Club
10% discount on participation in Fwdays conferences
Available installment payments from Monobank and installment purchases from PrivatbankIf after the first lesson you realize that your expectations do not match, we will refund your money in full.