“Lots of people and companies are sleeping on the power of synthetic data. LLMs are ridiculously good at generating synthetic data but it’s not straightforward plus we need more novel and complex data for not only improving LLMs but also systems built on LLMs (agents, RAG, etc.)
“Finally, we can systematically stress-test LLM’s reading comprehension abilities at scale, exposing exactly where these systems fail when trying to find and use information from documents. Paper – “RAGProbe: An Automated Approach for Evaluating RAG” 🔬 Introduces technique to
LlamaIndex and Box are two great tools that work great together!
🔍 Use Box AI prompt tool to query documents without downloading
📊 Extract structured data from unstructured content with Box AI extract
🔗 Seamlessly integrate Box AI with LlamaIndex agents
“Dropping the first few videos on my knowledge assistant video series 👇 Step 1: Figure out how to define an agentic workflow on top of your standard RAG endpoints that can use LLMs to reason before your retrieval layer and afterwards. This lets you build more sophisticated
“Build a serverless RAG app in just 9 lines of code with @DBOS_Inc and LlamaIndex! Learn how to: 🚀 Deploy AI apps to the cloud with a single command 💰 Reduce costs by 50x compared to AWS Lambda 🔄 Create resilient AI applications with durable execution This tutorial shows you
[2410.18712v1] Retrieval-Augmented Diffusion Models for Time Series Forecasting
“Our industry-leading AI search model is now multimodal! Embed 3 enables enterprises to build systems that can accurately and quickly search across both text and image data sources like complex reports, product catalogs, and design files. https://x.com/cohere/status/1848760845641388087
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