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RAG and Streamlit Chatbot: Chat with Documents Using LLM

Analytics Vidhya

Introduction This article aims to create an AI-powered RAG and Streamlit chatbot that can answer users questions based on custom documents. Users can upload documents, and the chatbot can answer questions by referring to those documents.

Chatbots 308
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30+ LLM Interview Questions and Answers

Analytics Vidhya

Introduction Large Language Models (LLMs) are becoming increasingly valuable tools in data science, generative AI (GenAI), and AI. LLM development has accelerated in recent years, leading to widespread use in tasks like complex data analysis and natural language processing.

LLM 322
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Hugging Face Launches Open Medical-LLM Leaderboard to Evaluate GenAI in Healthcare

Analytics Vidhya

Generative AI models hold promise for transforming healthcare, but their application raises critical questions about accuracy and reliability. Hugging Face has launched an Open Medical-LLM Leaderboard aiming to address these concerns.

LLM 344
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10 Open Source Datasets for LLM Training

Analytics Vidhya

Introduction As you may know, large language models (LLMs) are taking the world by storm, powering remarkable applications like ChatGPT, Bard, Mistral, and more. But have you ever wondered what fuels these robust AI systems? The answer lies in the vast datasets used to train them.

LLM 312
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How to Leverage AI for Actionable Insights in BI, Data, and Analytics

In the rapidly-evolving world of embedded analytics and business intelligence, one important question has emerged at the forefront: How can you leverage artificial intelligence (AI) to enhance your application’s analytics capabilities? Infusing advanced AI features into reports and analytics can set you apart from the competition.

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Mistral AI unveils LLM rivalling major players

AI News

Mistral AI, a France-based startup, has introduced a new large language model (LLM) called Mistral Large that it claims can compete with several top AI systems on the market. Mistral AI stated that Mistral Large outscored most major LLMs except for OpenAI’s recently launched GPT-4 in tests of language understanding.

LLM 243
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Overcoming LLM Hallucinations Using Retrieval Augmented Generation (RAG)

Unite.AI

Large Language Models (LLMs) are revolutionizing how we process and generate language, but they're imperfect. Just like humans might see shapes in clouds or faces on the moon, LLMs can also ‘hallucinate,' creating information that isn’t accurate. Let’s take a closer look at how RAG makes LLMs more accurate and reliable.

LLM 290
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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. Aarushi Kansal, AI leader, is here to explore ongoing testing and evaluation strategies tailored specifically for LLM-based applications.