RQle.AI’s Post

🤔Are standard #AI benchmarks failing your business? My latest blog explores why a 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫-𝐜𝐞𝐧𝐭𝐫𝐢𝐜 and 𝐞𝐭𝐡𝐢𝐜𝐚𝐥 approach to LLM evaluation is crucial for real-world success. #GenerativeAI #ResponsibleAI #EthicalAI #AIforBusiness #DataDrivenBusiness #PassionforAI

Large Language Models (LLMs) do not need any introduction with the rise of its massive adoption in the industry. Most enterprises have either adopted or are planning to adopt an LLM to build Generative AI-based enterprise applications supporting a variety of business use cases. Read more here 👇 https://lnkd.in/gibmVs2e

Knowledge graphs can help capture complex relationships between entities, providing meaningful context for large language models (LLMs) and their downstream data sets. This article https://lnkd.in/dabxWb6s by my colleagues Jorge Machado , Kayvaun Rowshankish and others is a great read for a playbook on the how.

Building a starter RAG (Retrieval-Augmented Generation) chatbot is easy; the hard part is to make it not hallucinate.   RAGs can learn from sources, such as PDFs. These PDFs are chunked and stored in vector stores. On each prompt, similar documents are fetched and passed to the LLM to provide an answer.   At times, the RAG bot might hallucinate because the data it retrieves from the documents might not be the best data to serve to the LLM.   So how do we stop this hallucination?   Advanced retrieval techniques like Sentence-window retrieval and auto-merging retrieval are some of the options to go for.   In Sentence-window retrieval, we chunk the data into sentences, and also store these sentences with the meta-data of the previous and next context.   While in Auto-merging retrieval, chunks are created as parent and child nodes. The parent node is the collection of multiple child nodes and provides more information to the LLMs for better understanding.   These techniques are classified under Advanced RAG and are a step up to the regular random chunking and storing of document information.   Once the data is chunked and the RAG chatbot retrieves information; you would want an automated approach to ensure the bot is not giving out less relevant information.   TruLens is one such tool that makes this step smooth.   It uses 3 major matrix points to give you an understanding of how the chatbots are performing;   Groundedness: Measures how well-grounded the generated answers are in the documents.   Context relevance: Gauges the relevancy of the retrieved context, calculated based on both the question and contexts.   Response relevance: This evaluation metric focuses on assessing how pertinent the generated answer is to the given prompt.     All the matrices are calculated on a 0 to 1 scale, where 1 means yay! And 0 means nah.   Deeplearning.ai short course is the best way I found to digest these topics.   https://lnkd.in/g5cNkeR7 #LearnAndShare #GenAI #RAG

Financial services firms are adopting Large Language Models (LLMs) to: · Modernize their legacy systems · Streamline and automate processes · Enhance data processing capabilities and customer service standards · Create content in knowledge domains. The choice of model directly influences the performance and effectiveness of the intended use case, with optimal resource allocation and compliance with regulatory standards as key considerations. More information here: #AI #Genai #LargeLangageModels #GenerativeAI #OpenSource #technology

🤖 Reducing bias in LLM evaluations: How blind comparisons cut through bias Choosing the right large language model (LLM) is tricky—bias can sneak in and skew decisions. Public leaderboards or benchmarks, while a great starting point, often fail to reflect the accuracy, cost-effectiveness, or speed required for your specific domain. Our LLM comparison tool offers a better way to evaluate what truly matters for your use case. Here’s how it makes a difference: 🔍 Objectivity in Action: Blind testing removes names and reputations, allowing you to evaluate responses based purely on their quality—no preconceptions, just results. ⚖️ Leveling the Playing Field: All models, regardless of popularity or hype, are judged equally in a blind test. This is especially valuable when assessing emerging or underrepresented models. 🌍 Inclusive Evaluations: By enabling side-by-side testing, blind comparisons empower users to discover the best-performing models for their specific use cases, including those from regions like Southeast Asia, often overlooked in global assessments. 🔓 Transparency and Trust: When results and evaluation data are shared openly, the process becomes more transparent, fostering trust in the outcomes and supporting broader research and collaboration. Blind testing isn’t just a feature—it’s a step toward smarter, fairer, and more inclusive AI. 🚀 Excited to see which model delivers best for you? Head on over to eval.supa.so 😎 #LLMComparison #AIInsights #TechTransparency #AIEvaluation

Retrieval Augmented Generation (RAG) is the process of optimizing the output of large language models (LLMs), so it can extract information from documents in your knowledge base (your company's data) and answer your employees questions. Herve @ biZNov helps you make the right technology choices to bring process efficiency and competitive advantages to your company. #GenAI #RAG #LLM #efficiency #competitivity https://lnkd.in/gkK7D_76

Introducing the Reference Data & Standards Working Group! Their mission is to advance critical discussions and industry best practices for financial instrument and market participant reference data throughout the trade lifecycle. By collaborating with other industry groups and participants, they work towards: 🔹Continuing the review and modification of existing U.S. market practice for Standing Settlement Instructions (“SSIs”) 🔹Exploring the impact and potential use cases of Large Language Models (LLMs) and Artificial Intelligence (AI) within the reference data identification space. They're shaping the future of reference data accuracy and efficiency!  For more information, visit https://lnkd.in/erAtPWEc  #ISITC #WorkingGroups #ReferenceData #MarketStandards

Register here -bit.ly/48Uq9aR Tired of generic AI solutions that don't understand your unique business? Join us on March 7th for an exclusive virtual roundtable where we'll explore the exciting world of building Generative AI applications powered by your own data. In this interactive session, you'll discover: · The strategic advantage of leveraging Large Language Models (LLMs) with your proprietary data. · How to unlock the hidden potential of your vast data repositories. · Practical techniques for training and fine-tuning LLMs for domain-specific insights and superior accuracy. Our expert panel from SingleStore will help you chart the right path for building powerful, data-driven AI applications. Don't miss this opportunity to gain a strategic advantage in the age of AI! #artificialintelligence #machinelearning #dataanalytics #database #languagemodels #dataintegration #generativeai #aiintegration