Generative AI Use Cases

Large language models (LLMs) are typically optimized to answer peoples’ questions. But there is a trend toward models also being optimized to fit into agentic workflows. This will give a huge boost to agentic performance! Following ChatGPT’s breakaway success at answering questions, a lot of LLM development focused on providing a good consumer experience. So LLMs were tuned to answer questions (“Why did Shakespeare write Macbeth?”) or follow human-provided instructions (“Explain why Shakespeare wrote Macbeth”). A large fraction of the datasets for instruction tuning guide models to provide more helpful responses to human-written questions and instructions of the sort one might ask a consumer-facing LLM like those offered by the web interfaces of ChatGPT, Claude, or Gemini. But agentic workloads call on different behaviors. Rather than directly generating responses for consumers, AI software may use a model in part of an iterative workflow to reflect on its own output, use tools, write plans, and collaborate in a multi-agent setting. Major model makers are increasingly optimizing models to be used in AI agents as well. Take tool use (or function calling). If an LLM is asked about the current weather, it won’t be able to derive the information needed from its training data. Instead, it might generate a request for an API call to get that information. Even before GPT-4 natively supported function calls, application developers were already using LLMs to generate function calls, but by writing more complex prompts (such as variations of ReAct prompts) that tell the LLM what functions are available and then have the LLM generate a string that a separate software routine parses (perhaps with regular expressions) to figure out if it wants to call a function. Generating such calls became much more reliable after GPT-4 and then many other models natively supported function calling. Today, LLMs can decide to call functions to search for information for retrieval augmented generation (RAG), execute code, send emails, place orders online, and much more. Recently, Anthropic released a version of its model that is capable of computer use, using mouse-clicks and keystrokes to operate a computer (usually a virtual machine). I’ve enjoyed playing with the demo. While other teams have been prompting LLMs to use computers to build a new generation of RPA (robotic process automation) applications, native support for computer use by a major LLM provider is a great step forward. This will help many developers! [Reached length limit; full text: https://lnkd.in/gHmiM3Tx ]

How might generative AI support nonprofit workplace learning and upskilling? When OpenAI introduced ChatGPT Study and Learn Mode, it addressed a common concern in education: that AI makes it too easy for students to skip the thinking and jump to the answers. Study Mode turns ChatGPT into a learning buddy designed to help users articulate goals, reflect, and build skills step by step. Study Mode helps students explain what they know, identify where they’re stuck, and engage in a guided learning process. These same principles translate powerfully into nonprofit workplace learning. For example, a program manager preparing a theory of change can use Study Mode prompts to encourage deeper reasoning: "What assumptions are built into your model? How would you measure success?" By replacing instant answers with reflective dialogue, ChatGPT Study Mode discourages shallow thinking and could help staff strengthen strategic instincts. It’s a smart way to reinvest the time saved through AI automation. Nonprofit staff facing increased pressure to do more with less often turn to AI for automation to save steps on drafting content, summarizing meeting notes, or analyzing reports.AI can and should make our work more efficient.  But they’re only one way to collaborate with generative AI. Nonprofits also need to use AI augmentation or working with it collaboratively to support human intelligence.  AI can be a thinking partner, not just a productivity hack. When used well, generative AI can: Encourage staff to reason through problems Support learning through adaptive feedback Create space for deeper planning, strategy, and interpersonal connection Generative AI is primarily valued for speed and being frictionless. Cognitive offloading may save time in the short term, but over-reliance can dull strategic instincts and reduce our ability to make meaning across complex situations. In a sector where human judgment, pattern recognition, and values-based decision making matter deeply, that’s a risk we can’t afford. We have an opportunity to use generative AI tools to support upskilling strategies that enhance staff capability alongside human-to-human learning such as mentoring, team dialogue, and on-the-ground practice. AI isn’t a replacement, but it can be a partner in nonprofit workplace learning. https://lnkd.in/gs_rzEtR #AIAugmentation #HumanAICollaboration #AIskilling #Upskilling #humanskills #learning #workplacelearning Philip Deng Rachel Kimber, MPA, MS Meenakshi (Meena) Das Tim Lockie Kaz McGrath John Kenyon Chantal (Coco) Forster

Cutting through the AI noise - here are 5 use cases for using generative AI today in a law practice: 1) Having AI draft initial responses to standard discovery requests, pulling directly from client documents and past cases—turning 3 hours of document review into 20 minutes of attorney verification. 2) Using AI to analyze deposition transcripts and build detailed witness chronologies, flagging inconsistencies and potential credibility issues that could be crucial at trial. 3) Feeding settlement agreements from similar cases to AI to generate initial settlement terms, helping attorneys start negotiations with data-backed proposals rather than gut instinct. 4) Having AI review client intake forms and past matters to spot potential conflicts of interest—moving beyond simple name matching to identify subtle relationship patterns. 5) Using AI to draft routine motions and pleadings by learning from the firm's document history, maintaining consistent arguments while adapting to case-specific facts. The real value isn't replacing attorney judgment. It's eliminating the mechanical tasks that keep great lawyers from doing their best work. What specific AI applications are you seeing succeed (or fail) in your practice? #legaltech #innovation #law #business #learning

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?

From chatbots to code generation, and from creative design to personalized automation, Gen AI is transforming how machines understand, reason, and create. Generative AI is not just a buzzword anymore, it’s the engine driving innovation across industries. Here’s the break down 👇 Core Concepts - Gen AI runs on foundation models like GPT and Gemini that learn from massive datasets. - Concepts like Prompt Engineering, RAG, and Chain-of-Thought help AI think and plan effectively. - Techniques like Few-Shot, Zero-Shot, and Multi-Modal Learning enhance flexibility and cross-domain intelligence. How It Works - Gen AI uses Transformers and attention mechanisms to understand and generate context-aware output. - It relies on embeddings, sequence modeling, and RLHF to refine responses through feedback and learning. Applications - From text generation and AI chatbots to code writing and content creation, Gen AI boosts productivity and creativity everywhere. - It’s also revolutionizing design, education, and automation with intelligent, adaptive solutions. Challenges - Despite its power, Gen AI faces issues like bias, hallucination, and data privacy risks. - Scalability, context limits, and ethical use remain ongoing challenges for developers and businesses. The Future - Gen AI is evolving into Agentic AI, systems that can plan, reason, and collaborate independently. - Expect smarter models with memory, context awareness, and autonomous decision-making in the near future. Popular Tools - Top platforms include OpenAI, Anthropic Claude, Google Gemini, Meta LLaMA, and Mistral AI - Frameworks like LangChain, Hugging Face, and Stability AI simplify Gen AI development. In short: Gen AI bridges automation and intelligence - combining creativity, logic, and adaptability to shape a smarter future. #GenAI

Rethinking Vector Search: Beyond Nearest Neighbors with Semantic Compression and Graph-Augmented Retrieval Traditional vector databases rely on approximate nearest neighbor (ANN) search to retrieve the top-k closest vectors to a query. While effective for local relevance, this approach often yields semantically redundant results-missing the diversity and contextual richness required by modern AI applications like RAG systems and multi-hop QA. The Problem with Proximity-Based Retrieval: Current ANN methods prioritize geometric distance but don't explicitly account for semantic diversity or coverage. This leads to retrieval results clustered in a single dense region, often missing semantically related but spatially distant content. Enter Semantic Compression: Researchers from Carnegie Mellon University, Stanford University, Boston University, and LinkedIn have introduced a new retrieval paradigm that selects compact, representative vector sets capturing broader semantic structure. The approach formalizes retrieval as a submodular optimization problem, balancing coverage (how well selected vectors represent the semantic space) with diversity (promoting selection of semantically distinct items). Graph-Augmented Vector Retrieval: The paper proposes overlaying semantic graphs atop vector spaces using kNN connections, clustering relationships, or knowledge-based links. This enables multi-hop, context-aware search through techniques like Personalized PageRank, allowing discovery of semantically diverse but non-local results. How It Works Under the Hood: The system operates in two stages: first, standard ANN retrieval generates candidates, then a greedy optimization algorithm selects the final subset. For graph-augmented retrieval, relevance scores propagate through both vector similarity and graph connectivity using hybrid scoring that combines geometric proximity with graph-based influence. Real Impact: Experiments show graph-based methods with dense symbolic connections significantly outperform pure ANN retrieval in semantic diversity while maintaining high relevance. This addresses critical limitations in applications requiring broad semantic coverage rather than just local similarity. This work represents a fundamental shift toward meaning-centric vector search systems, emphasizing hybrid indexing and structured semantic retrieval for next-generation AI applications.

If you are an AI Engineer building production-grade GenAI systems, RAG should be in your toolkit. LLMs are powerful for information generation, but: → They hallucinate → They don’t know anything post-training → They struggle with out-of-distribution queries RAG solves this by injecting external knowledge at inference time. But basic RAG (retrieval + generation) isn’t enough for complex use cases. You need advanced techniques to make it reliable in production. Let’s break it down 👇 🧠 Basic RAG = Retrieval → Generation You ask a question. → The retriever fetches top-k documents (via vector search, BM25, etc.) → The LLM answers based on the query + retrieved context But, this naive setup fails quickly in the wild. You need to address two hard problems: 1. Are we retrieving the right documents? 2. Is the generator actually using them faithfully? ⚙️ Advanced RAG = Engineering Both Ends To improve retrieval, we have techniques like: → Chunk size tuning (fixed vs. recursive splitting) → Sliding window chunking (for dense docs) → Structured data retrieval (tables, graphs, SQL) → Metadata-aware search (filtering by author/date/type) → Mixed retrieval (hybrid keyword + dense) → Embedding fine-tuning (aligning to domain-specific semantics) → Question rewriting (to improve recall) To improve generation, options include: → Compressing retrieved docs (summarization, reranking) → Generator fine-tuning (rewarding citation usage and reasoning) → Re-ranking outputs (scoring factuality or domain accuracy) → Plug-and-play adapters (LoRA, QLoRA, etc.) 🧪 Beyond Modular: Joint Optimization Some of the most promising work goes further: → Fine-tuning retriever + generator end-to-end → Retrieval training via generation loss (REACT, RETRO-style) → Generator-enhanced search (LLM reformulates the query for better retrieval) This is where RAG starts to feel less like a bolt-on patch and more like a full-stack system. 📏 How Do You Know It's Working? Key metrics to track: → Context Relevance (Are the right docs retrieved?) → Answer Faithfulness (Did the LLM stay grounded?) → Negative Rejection (Does it avoid answering when nothing relevant is retrieved?) → Tools: RAGAS, FaithfulQA, nDCG, Recall@k 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d Image source: LlamaIndex

🌟 A Pragmatic Take on AI Applications 🌟 Generative AI is a powerful tool, but its true potential lies in practical applications that deliver real value. Here’s a thoughtful perspective on how businesses can leverage Generative AI effectively, inspired by insights from industry experts: 1. Focus on Tangible Use Cases 🎯 Generative AI should be applied to well-defined problems. For instance, in healthcare, AI can analyze medical records to identify patterns that lead to early diagnosis and personalized treatments. This targeted approach improves patient outcomes and optimizes healthcare resources. 2. Integration with Existing Systems 🔗 Rather than deploying AI as an isolated solution, it should be seamlessly integrated into existing workflows. In customer service, AI-driven chatbots can handle routine inquiries, allowing human agents to focus on more complex issues that require empathy and critical thinking. This integration enhances service efficiency and customer satisfaction. 3. Empowering Employees 🧑💼 AI should augment human capabilities, not replace them. By handling repetitive tasks, AI frees up employees to engage in more strategic and creative activities. For example, marketers can use AI to analyze customer data and develop personalized campaigns, enhancing engagement and conversion rates. 4. Leveraging Data for Insights 📊 Generative AI excels at processing large datasets to uncover actionable insights. In finance, AI can analyze market trends and predict risks, enabling more informed investment decisions. This data-driven approach reduces uncertainty and enhances strategic planning. 5. Ethical and Responsible AI Practices ⚖️ Deploying AI responsibly is crucial. This means ensuring transparency, protecting data privacy, and addressing biases in AI algorithms. Ethical AI practices build trust with customers and stakeholders, fostering a positive reputation and long-term success. 6. Practical Examples of AI in Action 🏥 Healthcare: AI models predict patient deterioration, allowing timely interventions and better resource management in hospitals. 📚 Education: AI-powered platforms personalize learning experiences, improving student outcomes by adapting content to individual needs. 🛍️ Retail: AI-driven recommendation systems boost e-commerce sales by offering personalized shopping experiences. 🤔 Final Thoughts: Generative AI’s true value emerges when it’s applied thoughtfully and strategically. By addressing specific needs, integrating seamlessly with existing systems, empowering employees, leveraging data for informed decisions, and maintaining ethical standards, businesses can unlock AI’s full potential.💡 Subscribe to the Generative AI with Varun newsletter for more practical insights: 🔗 https://lnkd.in/gXjqwQaz Thanks for joining me on this journey! #GenerativeAI #EthicalAI #Applications

Let’s talk about some real potential of Generative AI. Here are 9 Use cases a business leader should know to understand how to extract real value out of Gen AI. 𝟭. 𝗔𝘀𝘀𝗲𝘁 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 ↳ Optimize and Simulate maintenance schedules using historical use and performance data. ↳ Benefits - Cost Improvements - Better Health & Safety - Increased throughput 𝟮. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝘁𝗿𝗮𝗱𝗲 𝗽𝗿𝗼𝗺𝗼𝘁𝗶𝗼𝗻𝘀 ↳ Prepare negotiation decks and analyze vast amounts of historic unstructured data to support the negotiation process ↳ Benefits - Efficient trade promo process - Better allocation of resources - Data-driven decision making 𝟯. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 ↳Fast design iterations using design software (Creative Assistant). Add insights from historical market data. ↳Benefits - Faster Speed-to-market - ‘More Creative Bandwidth’ - Curtailing market research time 𝟰. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 ↳Locally fine-tuned models enable faster access to information through human-like interaction. ↳Benefits - Data-driven decision making - Analyze previously inaccessible unstructured data 𝟱. 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 ↳Faster migration to advanced analytics through assisting code development ↳Benefits - Short software dev lifecycle - Access to a wider knowledge base for SMEs 𝟲. 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 ↳ Generate synthetic data for testing and simulating scenarios previously unknown. ↳ Benefits - Faster AI Model deployment - Rigorous testing using scores of data 𝟳. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝘃𝗲𝘀 ↳ Using NLP, Speech-to-text deploys 24-hour Customer support. ↳ Benefits - Better customer experience - Increased human Customer Representative’s efficiency 𝟴. 𝗣𝘂𝗯𝗹𝗶𝗰 𝗦𝗲𝗰𝘁𝗼𝗿 𝗨𝗿𝗯𝗮𝗻 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 ↳ Support Governments to simulate scenarios of various infrastructure decisions. Generate 3D models for master planning. ↳ Benefits - Super-charge creativity - Better decision-making Faster ideas generation 𝟵. 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 ↳Multi-national corporations get access to huge in-house content and best practices previously in different languages ↳ Benefits Better Customer experience Best-practice sharing Standardized processes Share what else you can add. If you like the post, share it with someone who can benefit from it. --- I am Tariq Munir...My mission is to create a Tech-enabled Humanistic future for all through my talks, writings, and content. Follow me to be part of this mission and learn more about Digital Transformation, Data, and AI.

Can large language models be used in biotech? The short answer is yes. While LLMs are often associated with chatbots, their capabilities extend beyond that. In biotech, much of the data comes in the form of sequences – like nucleotides in DNA, or amino acids in proteins. Similar to sentences in natural language, these biological sequences have unique semantic meanings based on the arrangement of their components. When input data is fed into an LLM, a transformer converts these sequences into contextual vectors using its attention mechanism. This process allows the model to understand the context and relationships within the data, enabling it to predict subsequent elements. One such use case is prediction of neoantigens that enable targeting tumor cells in personalized cancer immunotherapies. Neoantigens are tumor-specific mutated peptides presented on the surface of tumor cells because they bind to human leukocyte antigen (HLA) molecules. LLMs can predict this binding affinity. This allows the development of personalized therapies that use the patient's own immune system to kill tumor cells without damaging healthy tissues.