Building AI Agents with TARS & Weaviate

Weaviate vector database · Intermediate ·🔍 RAG & Vector Search ·9mo ago

Key Takeaways

Builds AI agents with TARS and Weaviate

Original Description

Welcome to AI Engineer Spotlight, the series where we showcase groundbreaking AI projects and the brilliant engineers behind them. In this episode, host Adam sits down with the creators of TARS, a revolutionary conversational AI platform that makes it easier than ever to build intelligent agents. You’ll see a live demo of TARS in action — from connecting agents to knowledge bases and tools like Google Sheets, to handling real user queries with semantic search powered by Weaviate. The team also shares insights on prompt design, tool integration, and how to package documentation into a retrieval system that fuels powerful conversational agents. Whether you’re building customer support bots, knowledge assistants, or next-gen AI applications, this episode is packed with practical takeaways and inspiration. 👉 Explore TARS: https://hellotars.com 👉 Learn more about Weaviate: https://weaviate.io Don’t forget to like, share, and subscribe for more stories from the builders shaping the future of AI. 00:00 Intro – Welcome to AI Engineer Spotlight 00:17 Meet the creators of TARS 00:39 Demo overview: Agent, Knowledge & Tools 00:58 Connecting business knowledge & documents 01:12 Using Google Sheets as an AI tool 01:53 Live conversation demo with the agent 04:54 Knowledge retrieval tool on Weaviate Documentation 05:24 How Weaviate vector database powers search 06:31 Building knowledge retrieval tools 06:47 Connecting 300+ tools (Notion, Google Drive & more) 07:09 The challenge of prompt engineering 07:20 Auto-generating prompts & welcome messages 08:14 Wrapping up: Making AI agents accessible 08:26 Closing thoughts & where to try TARS
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
5 RAG Optimization Techniques Every AI Engineer Should Know In 2026
Optimize Retrieval-Augmented Generation (RAG) systems using 5 techniques: metadata filtering, ANN search, embedding caching, async retrieval, and quantization, to improve performance and accuracy
Medium · AI
📰
5 RAG Optimization Techniques Every AI Engineer Should Know In 2026
Optimize RAG models using 5 key techniques for improved performance and efficiency, essential for AI engineers working with Retrieval-Augmented Generation
Medium · Machine Learning
📰
Let’s talk about RAG: Why it exists, how it works and lot more about it.
Learn about RAG, its purpose, and how it works, to improve your understanding of this technology
Medium · RAG
📰
RAG - Semantic Caching
Learn how semantic caching in RAG improves query efficiency by storing previous search results in a cache, reducing the need for repeated vector database searches
Dev.to AI

Chapters (14)

Intro – Welcome to AI Engineer Spotlight
0:17 Meet the creators of TARS
0:39 Demo overview: Agent, Knowledge & Tools
0:58 Connecting business knowledge & documents
1:12 Using Google Sheets as an AI tool
1:53 Live conversation demo with the agent
4:54 Knowledge retrieval tool on Weaviate Documentation
5:24 How Weaviate vector database powers search
6:31 Building knowledge retrieval tools
6:47 Connecting 300+ tools (Notion, Google Drive & more)
7:09 The challenge of prompt engineering
7:20 Auto-generating prompts & welcome messages
8:14 Wrapping up: Making AI agents accessible
8:26 Closing thoughts & where to try TARS
Up next
LLM Wiki vs RAG Explained | Complete LLM Wiki Implementation Guide
Pavithra’s Podcast
Watch →