About us
A group for experienced and aspiring data professionals.
Join our Slack: https://datatalks.club/slack.html
Upcoming events
8
Vector Databases: Embeddings, Semantic Search, and Hybrid Retrieval
·OnlineOnlineThis is the 2nd workshop in our series to update the LLM Zoomcamp content.
This workshop updates Module 3: Vector Search.
In this hands-on session, Alexey Grigorev will show how to add semantic search to a RAG application using embeddings and a vector database.
You’ll learn how to turn text into embeddings, index them, search for semantically similar documents, and use the results as context for an LLM.
What you’ll learn:
- What vector search is and how it differs from keyword search
- What embeddings are and how they represent text
- How to embed FAQ documents for semantic retrieval
- How to index text data in a vector database
- How to run semantic search over indexed documents
- How to use vector search inside a RAG pipeline
- How to compare vector search with keyword search
- How hybrid search combines semantic and keyword retrieval
- When vector search works well and where it can fail
- How retrieval quality affects the final LLM answer
By the end, you’ll have a RAG pipeline that uses vector search to retrieve semantically relevant documents and generate answers based on them.
Like the other workshops, this will be a live demo with practical tips and time for Q&A.
***
All events in these series:
- Build Your First RAG Application with LLMs
- From RAG to AI Agents: Function Calling and Tool Use
- Vector Databases: Embeddings, Semantic Search, and Hybrid Retrieval
- RAG and Agents Evaluation: Measuring Retrieval and LLM Answer Quality
- Monitoring LLM Applications: Traces, Feedback, and Production Quality
***
## Thinking about Joining LLM Zoomcamp?
This workshop covers the updated content for Module 3 of the LLM Zoomcamp, our free course on building practical LLM applications with RAG, vector search, evaluation, monitoring, and AI agents.
You start with a simple RAG pipeline, then improve it with better retrieval, semantic search, function calling, evaluation, monitoring, and production practices.
The course covers the full lifecycle of an LLM application: from the first working prototype to evaluation, monitoring, and a complete final project.
The new cohort of LLM Zoomcamp starts on June 8, 2026. You can join it by registering here.##
## About the Speaker
Alexey Grigorev is the Founder of DataTalks.Club and creator of the Zoomcamp series.
Alexey is a software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books, including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS’17 Criteo Challenge.
Join our Slack: https://datatalks.club/slack.html
82 attendees
From RAG to AI Agents: Function Calling and Tool Use
·OnlineOnlineThis is the 3rd workshop in our series to update the LLM Zoomcamp content.
This workshop updates Module 2: Introduction to Agents.
In this hands-on session, Alexey Grigorev will show how to turn a basic RAG application into an agentic AI assistant.
You’ll start with a simple RAG pipeline over Zoomcamp FAQ documents, then add agentic behavior: search decisions, multiple tool calls, function calling, and structured interaction with external tools.
What you’ll learn:
- How to build a basic RAG application over course FAQ documents
- What makes a RAG flow “agentic”
- How agents decide when to search and when to answer
- How to make the LLM generate search queries based on the user question
- How to run agentic search over multiple iterations
- How to use previous actions and search history as context
- How OpenAI function calling works
- How to define tools that an LLM can call
- How to let an assistant search the FAQ database using tools
- How to add multiple tools, including a tool for adding new FAQ entries
- How to structure the assistant logic into reusable components
- How to use PydanticAI to simplify tool definitions and agent implementation
By the end, you’ll have an agentic course assistant that can search the FAQ database, decide when it needs more information, call tools, and answer student questions with context.Like the other workshops, this will be a live demo with practical tips and time for Q&A.
***
All events in these series:
- Build Your First RAG Application with LLMs
- From RAG to AI Agents: Function Calling and Tool Use
- Vector Databases: Embeddings, Semantic Search, and Hybrid Retrieval
- RAG and Agents Evaluation: Measuring Retrieval and LLM Answer Quality
- Monitoring LLM Applications: Traces, Feedback, and Production Quality
***
## Thinking about Joining LLM Zoomcamp?
This workshop covers the updated content for Module 2 of the LLM Zoomcamp, our free course on building practical LLM applications with RAG, vector search, evaluation, monitoring, and AI agents.
You start with a simple RAG pipeline, then improve it with better retrieval, semantic search, function calling, evaluation, monitoring, and production practices.
The course covers the full lifecycle of an LLM application: from the first working prototype to evaluation, monitoring, and a complete final project.
The new cohort of LLM Zoomcamp starts on June 8, 2026. You can join it by registering here.##
## About the Speaker
Alexey Grigorev is the Founder of DataTalks.Club and creator of the Zoomcamp series.
Alexey is a software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books, including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS’17 Criteo Challenge.
Join our Slack: https://datatalks.club/slack.html
95 attendees
RAG and Agents Evaluation: Measuring Retrieval and LLM Answer Quality
·OnlineOnlineThis is the 4th workshop in our series to update the LLM Zoomcamp content.
This workshop updates Module 4: Evaluation.
In this hands-on session, Alexey Grigorev will show how to evaluate retrieval and answer quality in a RAG application.
You’ll learn how to create ground truth data, evaluate search results, compare generated answers, and use both embedding-based metrics and LLM-as-a-Judge for offline evaluation.
What you’ll learn:- Why evaluation is important for LLM applications
- What can go wrong in RAG systems without systematic evaluation
- How to create ground truth data for retrieval evaluation
- How to use an LLM to generate evaluation data
- How to evaluate text search results
- How ranking metrics work for retrieval evaluation
- How to compare offline and online evaluation
- How to generate data for offline RAG evaluation
- How to use embeddings and cosine similarity to compare answers
- How to compare answers from different models
- How to use LLM-as-a-Judge for answer evaluation
- How to evaluate answers with A→Q→A’ and Q→A approaches
By the end, you’ll understand how to measure the quality of a RAG system instead of relying only on manual testing. You’ll have notebooks and datasets for evaluating both retrieval and generated answers.
Like the other workshops, this will be a live demo with practical tips and time for Q&A.***
All events in these series:
- Build Your First RAG Application with LLMs
- From RAG to AI Agents: Function Calling and Tool Use
- Vector Databases: Embeddings, Semantic Search, and Hybrid Retrieval
- RAG and Agents Evaluation: Measuring Retrieval and LLM Answer Quality
- Monitoring LLM Applications: Traces, Feedback, and Production Quality
***
## Thinking about Joining LLM Zoomcamp?
This workshop covers the updated content for Module 4 of the LLM Zoomcamp, our free course on building practical LLM applications with RAG, vector search, evaluation, monitoring, and AI agents.
You start with a simple RAG pipeline, then improve it with better retrieval, semantic search, function calling, evaluation, monitoring, and production practices.
The course covers the full lifecycle of an LLM application: from the first working prototype to evaluation, monitoring, and a complete final project.
The new cohort of LLM Zoomcamp starts on June 8, 2026. You can join it by registering here.## About the Speaker
Alexey Grigorev is the Founder of DataTalks.Club and creator of the Zoomcamp series.
Alexey is a software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books, including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS’17 Criteo Challenge.
Join our Slack: https://datatalks.club/slack.html
74 attendees
Past events
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