If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/7ws1ucdG9HAvi Chawla (@_avichawla):A popular LLM interview question:"Explain the 4 stages of training LLMs from scratch."(step-by-step explanation below) https://t.co/43WiCQuJfc ...

RT Nick Szabo (@NickSzabo4)@BillAckman This is such a bizarre hallucination. Has Bill Ackman been replaced by an LLM? ...

RT Nick Szabo (@NickSzabo4)@BillAckman This is such a bizarre hallucination. Has Bill Ackman been replaced by an LLM? ...

RT Nick Szabo (@NickSzabo4)@BillAckman This is such a bizarre hallucination. Has Bill Ackman been replaced by an LLM? ...

Hi everyone, thanks Tok for having me. I hope everyone is doing well. I'm a PhD student at Alers One University in AI. I'm also a teaching assistant at Oran One University and Alis one. I am also a software developer and uh been a networking engineer at URI.So I know it's not like the most obvious story when it comes to a tech persona but uh it has been definitely not a very stable career for someone who has started with a networking engineer and then turned to web and then to to AI and yeah the world of te ...

Hugging Face CEO says we’re in an ‘LLM bubble,’ not an ‘AI bubble’ https://t.co/mKirsu3wzV ...

RT Avi Chawla (@_avichawla)RAG vs. Graph RAG, explained visually!RAG has many issues.For instance, imagine you want to summarize a biography, and each chapter of the document covers a specific accomplishment of a person (P).This is difficult with naive RAG since it only retrieves the top-k relevant chunks, but this task needs the full context.Graph RAG solves this.The following visual depicts how it differs from naive RAG.The core idea is to:- Create a graph (entities & relationships) from documents.- Trave ...

RT Tesla Owners Silicon Valley (@teslaownersSV)BREAKING: Grok Rankings Update November 13Grok is dominating every leaderboard:#1 BlackBox AI#1 Terminal-Bench Hard#1 GPQA Diamond#1 SciCode#1 AAII Token Usage#1 Roo Code#1 KiloCode#1 ClineAnd on OpenRouter:#1 Most popular LLM (English)#1 Token usage (Top Today, Week, Month)#1 Programming use case (Python, JS, Java, C++, SQL…)#1 Market share for xAIGrok is cooking. ...

RT Tesla Owners Silicon Valley (@teslaownersSV)BREAKING: Grok Rankings Update November 13Grok is dominating every leaderboard:#1 BlackBox AI#1 Terminal-Bench Hard#1 GPQA Diamond#1 SciCode#1 AAII Token Usage#1 Roo Code#1 KiloCode#1 ClineAnd on OpenRouter:#1 Most popular LLM (English)#1 Token usage (Top Today, Week, Month)#1 Programming use case (Python, JS, Java, C++, SQL…)#1 Market share for xAIGrok is cooking. ...

RAG Challenges & HyDE Solution - Traditional RAG faces challenges due to semantic dissimilarity between questions and answers, leading to irrelevant context retrieval [1] - HyDE addresses this by generating a hypothetical answer using an LLM, embedding it, and using the embedding to retrieve relevant context [2] - HyDE leverages contriever models trained with contrastive learning to filter out hallucinated details in the hypothetical answer [3] HyDE Performance & Trade-offs - Studies indicate HyDE improves retrieval performance compared to traditional embedding models [4] - HyDE implementation results in increased latency and higher LLM usage [4] HyDE Implementation - HyDE involves using an LLM to generate a hypothetical answer (H) for the query (Q) [2] - The hypothetical answer is embedded using a contriever model to obtain embedding (E) [2] - Embedding (E) is used to query the vector database and retrieve relevant context (C) [2] - The hypothetical answer (H), retrieved context (C), and query (Q) are passed to the LLM to produce a final answer [3]