Generative AI:
practical applications of RAG with LangChain

Retrieval-Augmented Generation (RAG)

Description

This comprehensive seminar explores the practical applications of Retrieval-Augmented Generation (RAG) using LangChain and LangSmith. RAG is a powerful technique that combines the generative capabilities of Large Language Models (LLMs) with the precision of information retrieval, enabling sophisticated question-answering and data interaction applications.

This course emphasizes hands-on learning, equipping participants with the skills to build robust RAG systems for various practical scenarios. You will learn how to use OpenAI without showing them your data!

What you will learn

This comprehensive seminar explores the practical applications of Retrieval-Augmented Generation (RAG) using LangChain and LangSmith. RAG is a powerful technique that combines the generative capabilities of Large Language Models (LLMs) with the precision of information retrieval, enabling sophisticated question-answering and data interaction applications.

This course emphasizes hands-on learning, equipping participants with the skills to build robust RAG systems for various practical scenarios. You will learn how to use OpenAI without showing them your data!

Main Topics

  • Introduction to RAG
  • Setting Up Data Ingestion and Indexing
  • Building and Optimizing RAG Pipelines
  • Evaluating and Minimizing Hallucinations
  • Advanced RAG Techniques and Best Practices
  • Using LangSmith for Comprehensive Evaluation

Cost

€900,00

Date

12 - 13 Jun 2025

Timing: from 2 pm to 6 pm Italian time

Location

Online event

Book Event

Partecipation fee - 900€
Available Places: 100
The "Partecipation fee - 900€" ticket is sold out. You can try another ticket or another date.
Registration 30 days before the seminar date: 5% discount - 855,00€
Available Places: 100
The "Registration 30 days before the seminar date: 5% discount - 855,00€" ticket is sold out. You can try another ticket or another date.
Share on:
Facebook
Twitter
LinkedIn
Email
WhatsApp
Pocket
Reddit