วีดีโอ

Deep dive on why GraphRAG approach was created and how it works

advanced RAG techniques like RAPTOR and using Clues

Cursor, RepoAgent, Aider, and some more coding agents - overview and interesting architectural concepts

Technical review of the new Realtime API introduced on the dev day

How to migrate the llm provider in your AI app safely

If you need to build an llm-app that uses an open-source llm, this episode is for you. Intermediate level

end to end guide on how to get started and deploy to production your llm app

Build LLM-app with legal documents that have many references and domain verbiage

Getting started with LangGraph, the most popular multi agent framework

Utilize no-code tools for fast POCs

Review of why and how to build a multi-agent system. Assaf is Head of R&D @ Wix and GPT-researcher open source creator.

everything about adopting open source code and models for your llm app

build trust with users and stakeholders - transparency and explainability, reliability and consistency (how to fail safely), automation vs user control. how to evaluate with uncertainty, prompt engineering, effective bug reports

examples and practices of advanced agents, and use of LangGraph for effective tool usage by agents

27 - Latency optimization | Dor Ringel

26 - 12 RAG Pain Points and Proposed Solutions

25 - Reranking

24 - MVP best practices

23 - LLM theory | Mike Erlihson

22 - LangChain v0.1

21 - Knowledge Graph | Jesús Barrasa (Neo4j, ENG)

20 - RAG Evaluation | Jonathan Yarkoni

19 - RAG

18 - OpenAI Dev Day

17 - Evaluation | Philip Tannor (DeepChecks)

16 - Multi modal

15 - התאמת מתנדבים לפרויקטים בזמן המלחמה

14 - Prompt engineering

13 - Production environment | Idan Tovi (Vee)