Narrator的动态

Why do AI SQL agents not scale? SQL agents struggle to scale due to challenges in contextual understanding and metadata balance, making it hard for them to reliably convert questions into accurate SQL queries. Key points ?? 1. SQL Agent Definition: SQL agents convert written questions into SQL queries. Their complexity arises from the need to understand and navigate large, nuanced datasets. 2. Context Limitations: Human analysts remember context and nuances from conversations and interactions. SQL agents struggle because providing extensive context makes them inconsistent and prone to confusion. 3. RAG (Retrieval-Augmented Generation) Approach: RAG involves providing SQL agents with only relevant, similar context, but this often fails due to the nuanced nature of data. 4. Metadata Issues: Effective use of metadata (descriptions of tables and columns) is crucial but challenging. Poorly written metadata can cause confusion, while overly detailed metadata can obscure key differences. 5. Balancing Metadata: Companies often struggle to balance metadata detail, which impacts the SQL agent's ability to understand and utilize data effectively. Full Video Answer: https://lnkd.in/gGM8Z8WK #data #datamodelling #ai #genai #sqlagents #agents #sql #datascience

Read “From Retrieval to Intelligence: Exploring RAG, Agent+RAG, and Evaluation with TruLens“ by Vladyslav Fliahin on Medium: https://lnkd.in/gSKpkzU2

The most intelligent read I have seen in a long time https://lnkd.in/gCWh2dxE

Morning reading: https://lnkd.in/e5UrMBvY “By using the LLM-generated knowledge graph, GraphRAG vastly improves the “retrieval” portion of RAG, populating the context window with higher relevance content, resulting in better answers and capturing evidence provenance.”

What intrigues me about LLM’s is their emergent properties. ChatGPT is really good at giving medical advice, even though it wasn’t specifically trained to. One less visible emergent property is how LLMs implicitly build knowledge graphs. Knowledge graphs are difficult to build, whether on public, or private data. This new research from Microsoft uses this emergent property to significantly improve retrieval. It does this by allowing the language model to query using its own implicit knowledge graph. It’s like we are coming one step closer to speaking to the LLM in its own language and worldview. “Explain it to me like I’m a fifth grader” becomes “explain it to me like I am an LLM” This can only lead to better end performance. ?? Jonathan Larson

Best situation to raise our media competence, to work away from short attention span, to take visual data critically. Time to wake up from centuries-long naive faith in image. https://lnkd.in/eyQH4RFy

From Retrieval to Intelligence: Exploring RAG, Agent+RAG, and Evaluation with TruLens https://lnkd.in/d96xsEmQ

The Relevance of RAG in the Era of Long-Context LLMs https://lnkd.in/gDmPN7nU

From Retrieval to Intelligence: Exploring RAG, Agent+RAG, and Evaluation with TruLens https://lnkd.in/dmrAiHJn

Speed is of the essence in 2025, and Zeeshan Zia of Retrocausal couldn't be more ready. Learn what Zeeshan is doing at the intelligence augmentation system company here: https://retrocausal.ai/