How Large Language Models Drive Business Problem Solving

Large Language Models (LLMs) have quickly become the world's best interns and are accelerating toward becoming decent business analysts. A groundbreaking study by professors at the University of Chicago explores the potential of LLMs in financial statement analysis: • An LLM (GPT-4) outperformed human analysts in predicting earnings direction, achieving 60% accuracy vs 53% for analysts. • The LLM's predictions complement human analysts, excelling where humans struggled. This situation mirrors developments in medical imaging, where specific machine learning algorithms have shown superior performance to human radiologists in particular tasks, such as detecting lung nodules or classifying mammograms. Like in finance, these AI tools don't replace radiologists but complement their expertise • LLM performance was on par with specialized machine learning models explicitly trained for earnings prediction. • The LLM generated valuable narrative insights about company performance, not relying on memorized data. • Trading strategies based on LLM predictions yielded higher Sharpe ratios and alphas than other models. Beyond Financial Analysis, LLMs show promise in augmenting various areas of commercial analytics. For example, LLMS can process complex market dynamics, competitor actions, and transactional data to suggest optimal pricing strategies across product lines. Companies can leverage LLMs for rapid information synthesis (i.e., extracting critical points from large amounts of text/data), identifying anomalies, generating hypotheses, standardizing analyses, and personalized insights. Combined with Knowledge Graphs (LLMs + RAGs), they can be very powerful. Finance and other analytics professionals should explore integrating LLM-based analysis into their workflows. While LLMs show promise, human judgment remains crucial. Consider using LLMs to augment analysis, flag potential issues, and generate additional insights to enhance decision-making processes across finance, supply chain, marketing, and pricing strategies. As highlighted by Rob Saker, these findings underscore the potential for AI to revolutionize financial forecasting and business analytics more broadly. Every forward-thinking team should explore leveraging LLMs to enhance their analytical capabilities, decision-making processes, and operational efficiency. Please note, however, that while LLMs show great promise, they are not infallible, and this technology is still in the infant stages of "AI." They can produce convincing but incorrect information (hallucinations), may perpetuate biases present in their training data, and lack a true understanding of context. Human oversight, critical thinking, and domain expertise remain crucial in interpreting and applying LLM-generated insights. #revenue_growth_analytics #LLMs

Tech IQ: 1-LLMs Aren't Magic Wands (And That's Okay!) Today let's talk about something that's been buzzing in boardrooms: Large Language Models (LLMs) like ChatGPT, LLAMA, DeepSeek etc. They are incredible tools, but here is the catch, they are not plug-and-play magic. Let me explain with a story. Say a colleague asks, Why can't we just connect an LLM to our database and let it answer questions in plain English? Isn't that what AI does? ...Great vision. But here's what's missing in that mental model... 🔍 What LLMs Don't Know (Unless You Teach Them) LLMs aren't mind readers. Imagine handing someone a 1000 page book written in a language they don't speak and asking them to summarize it. That's an LLM without context. To talk to your data, it needs - Schema & Metadata: What do your table names mean? How are they connected? - Data Dictionaries: Is "revenue" called "Rev," "Sales," or "$$" in your system? - Data Profiles: What's normal vs. an outlier? Is "Q4" always the biggest quarter? Without this, the LLM is guessing. 🧩 The Invisible Workflow   Turning a casual question like "Show me last year's top-selling products by region" into an answer involves micro-steps:   1. Decoding what top-selling means (revenue? units sold?) 2. Joining 5+ tables (sales + inventory + customer data) 3. Filtering 10k rows without hitting token limits (yes, LLMs have text "budgets") 4. Explaining results in human language without misinterpreting numbers This isn't magic it's engineering. ⚙️ How Do We Actually Make It Work? Two paths: 1. RAG (Retrieval-Augmented Generation): Teach the LLM to "look up" answers in your data like a librarian. But first, you need organized shelves (clean data + clear metadata) 2. Fine-Tuning: Custom-train the model on your business's language. Think of it like teaching company jargons to a new hire Both need time, testing, and iteration. 💡 Key Takeaways for Leaders   1. LLMs need context they don't "learn" your business by osmosis 2. Token limits are real. Think of them as text message character limits… but stricter 3. Data quality matters. Garbage in = confusion out 4. Start small. Pilot a single use case (e.g. FAQs) before overhauling workflows 🚀 The Bigger Picture   LLMs are powerful, but they're like Formula 1 cars they need a skilled pit crew (your engineers) and a well-built track (your data infrastructure). The ROI? Huge. But it's a partnership, not a solo act. Next time someone says, "Let's just plug in the AI," smile and ask: "What's step one?" Tech IQ Mission: Simplify tech concepts for leaders. No jargon, no eye rolls just clarity. Refer my git repo for detailed process flow on using LLM for querying your data base. Got a topic you'd like me to discuss? Let me know! 👇

Large Language Models (LLMs) like ChatGPT have showcased their prowess and versatility across various industries, despite being introduced to the public just a year ago. This blog, authored by the Engineering team at Oscar Health, details their use of ChatGPT 4 in developing an insurance claim assistant function. This assistant is designed to answer customer queries about their claims effectively. In tackling this project, the team employed several unique strategies and solutions. Firstly, they translated complete claim information into a domain-specific language termed “Claim Trace,” enabling ChatGPT to convert structured data into natural language. To enhance the model's performance, they implemented a method akin to providing a table of contents, which aids ChatGPT in better understanding the structure of Claim Trace. Another strategy involved a chain-of-thought approach with function calling, directing ChatGPT to break down a complex problem into smaller, more manageable segments. Additionally, they incorporated an iterative retrieval function, prompting ChatGPT to seek further information in cases of high uncertainty, thereby ensuring more accurate responses. These three methodologies combined to yield great results. The team reported a 100% accuracy rate in simpler cases and over 80% accuracy in more complex scenarios. This achievement boosted the company's operational efficiency and demonstrated how to fine-tune LLMs like ChatGPT to effectively meet specific business objectives. – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Apple Podcast: https://lnkd.in/gj6aPBBY   -- Spotify: https://lnkd.in/gKgaMvbh #datascience #chatgpt #llm #finetuning #largelanguagemodels #engineering #healthcare https://lnkd.in/gRnf_KmV

What if you didn’t have to write SQL to get insights from your data? Imagine being able to _ask_ your database questions in plain language—no technical barriers, no SQL skills needed. With Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), we're making that possible. Here’s a peek into how it works: ➤ Schema Understanding: We extract and cache the database structure, giving the model the “map” it needs to understand your data. ➤ Enhanced Questions: Your natural language questions are enriched with this schema, so the model knows exactly what you’re asking. ➤ Relevant Results: A ranking model picks the most relevant tables, ensuring the model focuses on the correct data. ➤ SQL-Free Answers: The LLM generates SQL in the background, so you get accurate results without touching a single line of code. This isn’t just about tech—it’s about empowering everyone to explore data freely, making insights accessible and driving smarter decisions across teams. Could conversational AI make data analysis more effortless for you and your team? Cheers! Deepak Bhardwaj

🤖 𝗔𝗜 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱: 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗮 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝗮𝗻𝗱 𝗪𝗵𝘆 𝗦𝗵𝗼𝘂𝗹𝗱 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗖𝗮𝗿𝗲? Think of a large language model (LLM) as your company's most knowledgeable employee who never sleeps. But what exactly is it, minus the tech jargon? At its core, an LLM is AI software that understands and generates human language with remarkable sophistication. It's like having a universal translator that not only speaks your language but can write, analyze, and even code. Why should your business pay attention? Here are three game-changing capabilities: 1. Supercharged Customer Service: LLMs can handle customer inquiries 24/7, understand context, and provide personalized responses that sound natural – not robotic. One of our clients reduced response times by 80% while maintaining high satisfaction scores. 2. Knowledge Unlocked: Imagine instantly analyzing thousands of documents, contracts, or market reports. LLMs can summarize key insights, spot patterns, and answer specific questions about your data in seconds. 3. Productivity Amplified: From drafting emails to writing code to creating marketing content, LLMs act as an intelligent assistant that helps your team work smarter, not harder. Think of the time saved on routine tasks that could be redirected to strategic thinking. But here's what many miss: 𝘛𝘩𝘦 𝘳𝘦𝘢𝘭 𝘱𝘰𝘸𝘦𝘳 𝘰𝘧 𝘓𝘓𝘔𝘴 𝘪𝘴𝘯'𝘵 𝘪𝘯 𝘳𝘦𝘱𝘭𝘢𝘤𝘪𝘯𝘨 𝘩𝘶𝘮𝘢𝘯𝘴 – 𝘪𝘵'𝘴 𝘪𝘯 𝘢𝘶𝘨𝘮𝘦𝘯𝘵𝘪𝘯𝘨 𝘩𝘶𝘮𝘢𝘯 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴. When implemented thoughtfully, they free up your team to focus on what humans do best: creative problem-solving, relationship building, and strategic decision-making. 𝘛𝘩𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯 𝘪𝘴𝘯'𝘵 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘵𝘰 𝘢𝘥𝘰𝘱𝘵 𝘓𝘓𝘔𝘴, 𝘣𝘶𝘵 𝘩𝘰𝘸 𝘵𝘰 𝘪𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘦 𝘵𝘩𝘦𝘮 𝘦𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦𝘭𝘺 𝘪𝘯𝘵𝘰 𝘺𝘰𝘶𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘰𝘱𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴. Those who move early and wisely will have a significant competitive advantage. What are your thoughts on LLMs? How do you see them transforming your industry? #ArtificialIntelligence #BusinessInnovation #DigitalTransformation #FutureOfWork #AI #Technology

Large language models (#LLMs) like Open AI's Chat #GPT are great for some things. Write me a poem about the future of retail in the style of Shakespeare. But ask for last month's sales by product category, and it will either hallucinate or shrug its shoulders. Imagine if you knew everything that's lurking in your own data. What do our most loyal customers love about our product this month? Or how should we allocate $1M in spend to acquire high-value customers based on past performance? This gap is the distinction between the public large language models and enterprise IP inclusive of customer data, product performance, and marketing data. It's what we call a Large Knowledge Model (LKM), combining the conversational nature inherent in LLM yet powered by business data, enriched by our proprietary data assets, in a way that's safe, compliant, and, most critically, actionable. We're putting these GenCX models into place for our clients and already beginning to unlock uncommon insights, and putting new operating frameworks in place to unleash this intelligence across the org. While #GenCX is new, it builds on more than a decade of AI, automation, and customer identity innovations at #merkle . It may not be in the voice of Shakespeare, but I am excited for GenCX to meaningfully improve customer experiences and drive growth for our clients. And, as of this week, it's available to use with #salesforce Einstein GPT along with our recent expansion of generative AI tools via our recent deal with Microsoft's #Azure OpenAI platform. We try to bring some humanity and humility to the table by linking everything we explore to a problem to solve, starting typically a workshop that's equal parts education and inspiration along with a few prototypical use cases to see and experience. Learn more here:

𝐖𝐡𝐲 𝐘𝐨𝐮𝐫 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐚𝐚𝐒 𝐆𝐞𝐧 𝐀𝐈 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐈𝐬𝐧’𝐭 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐢𝐧𝐠 𝐆𝐫𝐞𝐚𝐭 𝐑𝐞𝐬𝐮𝐥𝐭𝐬? Vertical SaaS solutions, especially those outside of code tools, often operate in domains that fall well beyond the training scope of large language models (LLMs). Most LLMs are trained on publicly available datasets (think Reddit, Wikipedia, and general internet content), which are great for general-purpose language tasks but lack the nuanced understanding required for domain-specific problems. 𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 LLM embeddings are incredibly powerful for general language tasks. However, these same embeddings fall short when dealing with specialized terminologies and workflows unique to your business. For example, in a retail supply chain scenario: A delivery driver brings products to a store. A merchandiser stocks these products on shelves. In one business, these roles might overlap, while in another, they could be entirely distinct. Without a framework to guide the LLM, it’s likely to rely on generic knowledge that doesn't align with your operations, leading to subpar results. 𝐄𝐧𝐭𝐞𝐫 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡𝐬 They are powerful tools that allow us to create domain-specific context. A knowledge graph is essentially a network of nodes (concepts or entities) and relationships (connections between them), each enriched with metadata such as definitions, priority orderings, and additional context. This structured knowledge allows LLMs to move beyond their general training and focus on the specific needs of your business. The above example of a retailer, driver, and merchandiser is depicted in the image below. By leveraging knowledge graphs, you can: - Define business specific terminologies and relationships. - Provide LLMs with structured context that aligns with your problem space. - Prevent the LLM from defaulting to its internal (often incorrect) assumptions. - Dynamically inject context based on the data encountered for that run. 𝐖𝐡𝐲 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬 The same mechanisms that make embeddings powerful for general tasks (e.g., relational understanding like "king - man + woman = queen") can be harnessed within your business context. With a well-defined knowledge graph, the LLM isn't just "guessing" based on public data but is reasoning within the bounds of your domain-specific framework. Curious to hear how others have tackled similar challenges! #SaaS #GenAI #LLMs #knowledgegraphs #AI #startup

Do you rely on one large generalist model to power multiple use cases, or do you build a suite of specialized models fine-tuned for specific tasks? Large Language Models (LLMs) act as the generalists. One model can handle many functions across financial services: -Fraud Detection -Automated Investing -Customer Service Chatbots -Personalized Banking -Consumer Loan Underwriting -This flexibility makes them ideal for exploration, rapid prototyping, and -scenarios where breadth of understanding matters more than hyper-optimization. Small Language Models (SLMs) act as the specialists. Each is optimized for a single task, such as: -Loan Qualification -Consumer Loan Underwriting -Fraud Detection -The benefit? Efficiency, accuracy, and cost control. By narrowing the scope, SLMs can outperform generalist models in production environments where precision is non-negotiable. The Hybrid Future The reality isn’t LLM or SLM — it’s both. LLMs will serve as the reasoning engines, orchestrating complex workflows and bridging gaps across domains. SLMs will deliver deep expertise in critical tasks, ensuring enterprise-grade performance. This hybrid approach mirrors how organizations operate: broad leadership supported by domain experts. As AI adoption accelerates, companies that can strike the right balance between generalist adaptability and specialist efficiency will set the standard for the next wave of digital transformation. Question for you: In your industry, are you leaning more toward the power of generalist LLMs, the precision of SLMs, or a blended strategy?

Researchers have proposed an AI Training model called the Large Concept Model (LCM) that processes information very similar to humans, by understanding entire concepts hidden within sentences, not just words. They experimented with different ways to train the LCM, including using a method called "diffusion" which gradually adds noise to the sentence codes and then trains the model to remove the noise. And, they found that the LCM performs well on tasks like summarizing text and expanding short summaries into longer texts. It uses SONAR (Sentence-level multimodal and language-agnostic Representations), a system that works across 200+ languages and speech, to turn sentences into concepts. Their models are trained on these sentence-level embeddings to predict what comes next in a sequence, making it smarter and more efficient than traditional AI. But what makes it special: 👉More human-like understanding of language 👉Works across text and speech 👉Open-source code allows businesses to build custom solutions 👉More efficient than current AI models of similar size Why does this matter? Better Chatbots: This can enable smoother, more natural multilingual conversations with human nuance. Faster Problem Solving: LCMs will quickly summarize, expand, and tailor information for much more nuanced use cases, geographical regions, making problem solving more efficient.

The proliferation of Large Language Models (LLMs) in SaaS platforms promises some real potential for CX and business, generally. This could include automating customer interactions (e.g., generative surveys) to deriving actionable insights from vast datasets (generative insight delivery), LLMs are at the forefront of innovation. But how can we ensure we’re leveraging these capabilities to their fullest potential and TRUST the results? Here’s how I’m thinking about it (ACT): Accuracy Ensure the LLM has been trained on diverse, high-quality datasets. Ask for benchmarks and validation studies to assess its performance in real-world scenarios. Customization Look for platforms that allow you to fine-tune the model to your specific needs. Off-the-shelf solutions might not always cater to your unique business context. And you may want to bring your own model in, so look for platforms that give you choice. Transparency The best LLMs offer transparency into their decision-making processes. Opt for solutions that provide insights into how conclusions are reached, ensuring trust and accountability. Two other non-negotiables: With sensitive data at play, robust security measures and compliance with relevant regulations (e.g., GDPR, HIPAA). Most are already doing this. Finally, your SaaS partner has to offer strong customer support and have a team of experts to help you navigate the complexities of LLM integration and deployment. What else would you prioritize? #ai #SaaS #llm #customerexperience #dataanalytics #machinelearning #trust