Patronus AI’s Post

📌 The Power of Fine-Tuned Language Models Combined with RAG #generativeAI #RAG #LLM #insights In today’s fast-paced data landscape, getting the most out of language models means combining the strengths of different approaches. Imagine you start by fine-tuning a small language model with data specific to your industry—whether it’s finance, healthcare, or legal work. This process makes the model an expert in that field, giving it the ability to handle the unique language and complexities that come with it. But here’s where things get really interesting: when you pair this fine-tuned model with Retrieval-Augmented Generation (RAG), you take it to a whole new level. While your model already knows the ins and outs of your domain, RAG allows it to pull in fresh, relevant information from external sources on the fly. This means the model not only has deep, specialized knowledge but can also stay current and handle more complex or unusual queries with ease. The benefits are huge. You get a language model that’s both a specialist in its field and adaptable to new information as it comes in. This combination leads to more accurate, relevant, and scalable solutions that can drive better decision-making and improve operations across the board. It’s about harnessing the best of both worlds to create AI tools that are powerful, flexible, and perfectly suited to organizational needs.

A compact course on discovering when to use fine-tuning vs prompting for LLMs. This compact course covered key aspects like selecting open-source models, preparing data, and training & evaluating for specific domains. Thank you to the DeepLearning.AI team for creating such an insightful course. Looking forward to applying these knowledge in real-world projects! #MachineLearning #DeepLearning #LLMs #AI #Finetuning

Ever wondered how AI understands language like we do? 🤔 It all starts with embeddings, the secret sauce that transforms words into meaningful, machine-understandable numbers. From basic word relationships to deep context-based insights, embeddings are the reason Large Language Models (LLMs) can do everything from translating languages to writing like a pro. Curious to know how this works and why it’s a game-changer for LLMs? Check out this article where I break it all down! 👇 https://lnkd.in/eXDZiNGt #bid_challenge #bid_ai Break Into Data

The course covers the basics of embedding, chunking, keyword search, semantics search, reranking, and generating answers. Reranking ranks the results against the query so that LLM returns the top answer to users, boosting the accuracy of AI applications. #cohere #deeplearning.ai #reranking

~𝟳𝟬% 𝗼𝗳 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝘀 𝘂𝗻𝗱𝗲𝗿𝘂𝘁𝗶𝗹𝗶𝘇𝗲 𝘁𝗵𝗲 𝘃𝗲𝗿𝘆 𝘁𝗵𝗶𝗻𝗴 𝘂𝘀𝗲𝗿𝘀 𝗽𝗮𝘆 𝗳𝗼𝗿. Not always as dramatically as this image suggests, but it sure can feel that way. ⚠️ 𝗠𝗼𝘀𝘁 𝗔𝗜𝗠𝗦 𝗣𝗿𝗼𝗺𝗽𝘁 𝗦𝗲𝗮𝗿𝗰𝗵 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗮𝗿𝗲 𝟯 𝘄𝗼𝗿𝗱𝘀 𝗼𝗿 𝗹𝗲𝘀𝘀 The root of the problem is habit. There's also some fear, confusion, uncertainty. In this article, we look at 🗯️ why Prompt Search works best with tons of context (𝘦𝘷𝘦𝘯 𝘪𝘧 𝘪𝘵'𝘴 𝘤𝘩𝘢𝘰𝘵𝘪𝘤), ❌ misconceptions like "short queries lead to better/faster results" and 😳 worries around giving AI too much creative power. ➡️ 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂'𝗿𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗯𝘆 𝗻𝗼𝘁 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗮𝘆 𝘆𝗼𝘂 𝘀𝗲𝗮𝗿𝗰𝗵: https://lnkd.in/eKpUHgX4

Web Actions meet AI Actions 🤯 In Magic Inspector, you can create browser tests using natural language. Want to click on an element? Just select the "Click Element" action and describe the element you want to click on in natural language. This blend of AI and standard web components creates the ultimate experience for writing test flows. Test it for free 👇 https://lnkd.in/dRWr-Tzg

Friday Feature Preview: Natural language summaries in Zing Data! Ask a question visually or with natural language and get a text summary (along with the chart) that answers your question. Even better, they update every time you edit a chart. Seamlessly jump between natural language and visual drag + drop and Zing's AI summary stays up to date. In private preview, DM me for access.

I just published Vol. 70 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Instruction-Tuned Retrieval for Flexible Information Search, from Weller et al. 📚 Hierarchical Large Language Models for Enhanced Sequential Recommendations, from ByteDance 📚 Accelerating Long-Context LLMs via Dynamic Sparse Attention, from Liu et al. 📚 jina-embeddings-v3: A Multilingual, Long-Context Text Embedding Model, from Jina AI 📚 Personalizing Short-Video Search for Enhanced User Engagement, from Kuaishou 📚 Evaluating Multimodal Search Capabilities of Large Language Models, from Jiang et al. 📚 Evaluating Trustworthiness in RAG-Enhanced Large Language Models, from Zhou et al. 📚 Enhancing Recommender Systems with Interaction-Driven Sentence Transformers, from Vančura et al. 📚 An Adaptive Retrieval-Augmented System for Accurate AI Legal and Policy Interpretations, from Kalra et al. 📚 A Framework for Large-Scale Product Retrieval Assessment, from Zalando #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

👋 Hi everyone! PLMBR’s Contract Gen is powered by a mix of advanced AI assistants built with Groq, LlamaIndex, and Tavily. It’s designed to make contract creation simple, efficient, and tailored to your needs. In this quick video, we show you how the contract generation route works—from answering just 10 easy questions to making edits in real time using natural language commands. 📝✍️

Contract Gen is the first of many tools we are launching at Plmbr, designed to simplify contract management for everyone, not just legal professionals. What Contract Gen offers: Generate contracts of any length, covering a wide range of needs—from service agreements for HVAC and plumbing to contracts for electricians, nannies, lease agreements, and more Upload and edit existing contracts, enabling real-time modifications and seamless updates Conduct deep legal research, supported by real-time data on federal, state, and local laws, case law, and legislative updates Natural language processing, allowing users to query, update, or modify contracts simply by asking questions or giving commands in plain language Each contract is backed by the latest legal data, ensuring compliance with current regulations. Whether you’re a business owner, contractor, or someone in need of a contract, Contract Gen makes drafting, editing, and researching contracts fast, easy, and accessible for everyone. Contract Gen is powered by a group of AI assistants built with LlamaIndex Groq and Tavily While most of the assistants are powered by models from Groq a select few leverage our own fine-tuned version of Llama 3.1 70B, specifically optimized for handling complex legal tasks.