Patrick Atwater’s Post

A huge thanks to the Financial Times Data & Analytics team for organising a fantastic internal conference, 'Data Lab'. The day was dedicated to enhancing our understanding of AI, exploring the role of data in decision-making, and last but not least...the importance of good-quality data and data governance. It's been a productive day filled with valuable insights. Here's a couple of my key takeaways as a beginner in prompt engineering.... 1) Provide contextual background including persona 2) Use prompts to check the accuracy of your prompts or ask the prompt to be replayed to you so you can check it manually. 3) Save the prompts that you'll use regularly to save time in the future! (leveraging Open AI's 'GPT' feature) #FinancialTimes #Data #DataLab #AI

Improving the Capabilities of LLM-Based Analytics Copilots With Semantic Search and Fine-Tuning https://ift.tt/a1TBbI7 Picture this: You're an analyst drowning in a sea of data, trying to make sense of complex attribution models and customer journeys. Wouldn't it be great if you had a super-smart AI assistant that could instantly answer your questions, generate SQL queries on the fly, and break down complex tabular data? Well, that's exactly what we're working on with Large Language Model (LLM)- based analytics copilots. But as with any cutting-edge tech, it's not all smooth sailing. Let's dive into the challenges we faced and the cool solutions we came up with to make these AI assistants truly shine. The LLM Conundrum: Brilliant, but Flawed First things first: let's talk about why we're so excited about using LLMs in analytics. These language models are like the Swiss Army knives of the AI world – they can tackle a wide range of tasks, from answering questions to generating code. For us analysts, that means: via DZone AI/ML Zone https://dzone.com/ai-ml July 22, 2024 at 08:37AM

Power Your AI Models with the Right Data! Did you know? The effectiveness of AI depends on the quality of its training data. Web scraping provides: ✅ High-accuracy, domain-specific datasets ✅ Real-time updates for dynamic adaptability ✅ Industry-tailored insights for specialized AI use cases Stay ahead in the AI game by leveraging actionable data for smarter models. Learn more: https://lnkd.in/g_2qNJ7U #AITraining #WebScraping #AIInsights #PromptCloud #DataForAI

One new interesting application of LLMs: CAIS has released a forecasting bot that provides probability estimates for any question you ask. For example, to the question "Will GPT5 be released before 2025?", it answers that it's 70% likely. The bot works by searching for relevant information online, feeding it to GPT-4o, asking a few guiding questions, and then requesting a probability estimate. What's impressive is that this bot outperforms experienced human forecasters, even though GPT-4o wasn't specifically trained for this task. This kind of application shows that AI could be of great help in improving decision-making in the future. Link to the blog post: https://lnkd.in/gjP7JQgz Link the bot if you want to try it yourself: https://forecast.safe.ai/

The CAIS-developed FiveThirtyNine on GPT-4o is reportedly outperforming human forecasters with an impressive 87.7% accuracy on Metaculus. Just after the first presidential debate between Trump and Harris, it makes you wonder: Can AI-powered prediction capabilities answer questions like, "Who will win the coming US election?" Maybe AI is becoming the go-to for future forecasting? That would be fun to test. 😁 Explore FiveThirtyNine at https://lnkd.in/g5AVCg6k. Give it a try! #AI #GenAI #FiveThirtyNine

🚀 Unleash the Full Power of Your RAG System with These 5 Secret Hacks! 📈✨ Ready to take your Retrieval-Augmented Generation (RAG) system to the next level? Whether you're working on cutting-edge AI models or simply looking to fine-tune your data retrieval process, these five expert tips will supercharge your system's performance. Let's dive in! 1. Streamline and Structure Your Data 🧹 Clean Data, Better Results! The foundation of a powerful RAG system is well-organized, clean data. Ensure your knowledge base is logically structured and easy to navigate. Try leveraging a large language model (LLM) to create summaries of your documents and use these summaries to perform targeted searches. This technique will help you pinpoint the most relevant data quickly and efficiently. 2. Diversify Your Indexing Strategies 🗂️ Don’t Rely on Just One Method! While embedding-based similarity searches are great for context, don't forget about keyword-based searches for precision. Combining multiple indexing strategies allows you to handle specific queries more effectively, enhancing the accuracy and speed of your retrieval process. 3. Optimize Data Chunking 🧩 Find Your Perfect Chunk Size! The size of data chunks fed into your LLM can make or break your system's coherence and context understanding. Experiment with different chunk sizes to strike the perfect balance. Smaller chunks boost coherence, while larger ones capture more context. Fine-tuning this balance will significantly improve the relevance and quality of your outputs. 4. Implement Metadata for Filtering 🔍 Get the Most Relevant Results! Use metadata to add an extra layer of filtering in your retrieval process. Whether it's filtering by recency or other criteria, metadata ensures that your system retrieves the most relevant data first. This approach is particularly useful in chat or real-time applications where the latest information often holds the most value. 5. Utilize Query Routing and Reranking 🎯 Specialize and Prioritize for Precision! Enhance your retrieval accuracy by routing queries to specialized indexes and using reranking techniques. Imagine having a team of experts for different query types—whether you need detailed summaries or up-to-date information, query routing ensures your system consults the right “expert” every time. Want to dig deeper? Read the full blog post here: https://lnkd.in/ePbKa5Gp #AI #MachineLearning #DataScience #TechInnovation #ArtificialIntelligence #RAGSystems

I recently picked up this book looking for a comprehensive guide to understanding data and AI, and I was not disappointed. The author does a fantastic job of breaking down complex concepts into easy-to-understand explanations, making it accessible for readers of all levels. There are two remarkable phenomena that are unfolding almost simultaneously. The first is the emergence of a data-first world, where data has become a central driving force, shaping industries and fueling innovation.  #DataParadox #individual #enterprise https://amzn.to/4cyHzvE

Unlocking LLMs Superpowers with RAG Pipelines 🔐 In a recent blog post, we dissected the intricacies of Large Language Models (LLMs) and laid out a clear blueprint to seamlessly integrate these AI powerhouses with your business-specific data. LLMs have revolutionised the way we think about problem-solving in AI. They've shifted the dynamic from highly specific, data-intensive models to more adaptable, general-purpose frameworks. With these flexible tools, businesses can bypass the heavy lifting of data labelling and large-scale data science teams, leading to cost-effective, rapid application development. The beauty of LLMs lies in their ability to transcend industry barriers, offering out-of-the-box functionalities that can be fine-tuned to specific business needs.Redactive emphasises the right approach to LLMs—leveraging general model functionality and enhancing it with prompt engineering for a tailored result that truly enhances productivity and growth. Retrieval Augmented Generation (RAG) provides a streamlined method to infuse LLMs with business-specific context. It offers a simpler approach to introduce additional information to AI applications without the complexity of traditional data pipelines. RAG sidesteps limitations related to the volume and structural nature of information that can be prompted to an LLM. Redactive simplifies this further. Forget about the complex data management that comes with chunking and vector databases. Our managed RAG solutions ensure easy, secure integration with your document databases, effectively turning your proprietary information into the superfuel for LLMs. Read the full article below to dive deeper into the transformative potential of LLMs with RAG pipelines 👇 #LLMs #RAG #RAGpipeline #ArtificialIntelligence #DataIntegration #RedactiveAI #Technology #Innovation

What is 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆)? Imagine this: you’re asking the model something complex, and instead of just digging through what it learned months (or even years!) ago, it actually goes out, finds the freshest info, and brings it right back to you in its answer.  That’s Retrieval-Augmented Generation (RAG) in action. RAG is like an AI with a search engine built in.  Instead of winging it with just its trained data, it actively pulls in real-time facts from external sources and combines them with its own insights. The result? You get a response that’s not only coherent but packed with relevant, up-to-date information. How it works? 1. Query encoding: When a user inputs a question, it’s encoded into a format that a search engine or database can process.  The encoding turns the question into a vector or "embedding". 2. Retrieval phase: The retriever component then “searches” within an external database or document repository for relevant information. This step is critical as it brings in fresh, factual data, unlike traditional models that rely solely on pre-trained knowledge. The retrieved documents, often ranked by relevance, provide context for the response. 3. Generation phase: The embedding model takes both the initial query and the retrieved information. It compares these numeric values to vectors in a machine-readable index of an available knowledge base. Then it finds a match or multiple matches and retrieves the related data, converts it to words, and passes it back to the LLm. 4. Response generation: With retrieved data, LLM combines the retrieved words and crafts response as an answer to the user. Pros and Cons ➕ Pros: real-time access, improved accuracy, reduced hallucination, transparency ➖ Cons: complex implementation, increased latency, resource-intensive, dependency on data quality #ai #ml #llm #rag #techwithterezija

What is 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆)? Imagine this: you’re asking the model something complex, and instead of just digging through what it learned months (or even years!) ago, it goes out, finds the freshest info, and brings it right back to you in its answer. That’s Retrieval-Augmented Generation (RAG) in action. RAG is like an AI with a search engine built in. Instead of winging it with just its trained data, it actively pulls in real-time facts from external sources and combines them with its own insights. The result? You get a response that’s not only coherent but packed with relevant, up-to-date information. How it works? 1. Query encoding: When a user inputs a question, it’s encoded into a format that a search engine or database can process. The encoding turns the question into a vector or "embedding". 2. Retrieval phase: The retriever component then “searches” within an external database or document repository for relevant information. This step is critical as it brings in fresh, factual data, unlike traditional models that rely solely on pre-trained knowledge. The retrieved documents, often ranked by relevance, provide context for the response. 3. Generation phase: The embedding model takes both the initial query and the retrieved information. It compares these numeric values to vectors in a machine-readable index of an available knowledge base. Then it finds a match or multiple matches and retrieves the related data, converts it to words, and passes it back to the LLm. 4. Response generation: With retrieved data, LLM combines the retrieved words and crafts response as an answer to the user. Pros and Cons ➕ Pros: real-time access, improved accuracy, reduced hallucination, transparency ➖ Cons: complex implementation, increased latency, resource-intensive, dependency on data quality ——— ♻️ Repost to inspire your network 🖱️ Follow me, Terezija for daily posts #ai #machinelearning #ml #llm #rag #techwithterezija