Generative AI Use Cases

Developing a 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 can quickly become overwhelming without a solid foundation. A messy structure leads to inefficiency, making scaling and collaboration difficult.  𝗪𝗵𝗲𝗿𝗲 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗕𝗲𝗴𝗶𝗻?   To streamline development, I’ve designed a 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 that prioritizes 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻.  𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲  ✅ 𝗰𝗼𝗻𝗳𝗶𝗴/ – YAML-based configurations to separate settings from code.   ✅ 𝘀𝗿𝗰/ – Modularized core logic, including 𝗹𝗹𝗺/ and 𝗽𝗿𝗼𝗺𝗽𝘁_𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴/ components.   ✅ 𝗱𝗮𝘁𝗮/ – Organized storage for embeddings, prompts, and datasets.   ✅ 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀/ – Ready-to-use scripts for real-world use cases (e.g., chat sessions, prompt chaining).   ✅ 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀/ – Jupyter notebooks for rapid experimentation and analysis.  𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁  🔹 Use 𝗬𝗔𝗠𝗟 for clean, readable configurations.   🔹 Implement 𝗲𝗿𝗿𝗼𝗿 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 & 𝗹𝗼𝗴𝗴𝗶𝗻𝗴 for efficient debugging.   🔹 Apply 𝗿𝗮𝘁𝗲 𝗹𝗶𝗺𝗶𝘁𝗶𝗻𝗴 to manage API consumption effectively.   🔹 Maintain a 𝗰𝗹𝗲𝗮𝗿 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 for flexibility.   🔹 Optimize performance through 𝘀𝗺𝗮𝗿𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗰𝗮𝗰𝗵𝗶𝗻𝗴.   🔹 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 to ensure seamless team collaboration.   🔹 Leverage 𝗝𝘂𝗽𝘆𝘁𝗲𝗿 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀 for quick experimentation before production deployment.  𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗦𝘁𝗮𝗿𝘁𝗲𝗱  • Clone the repository & install dependencies.   • Configure your model using the provided YAML files (**config/**).   • Explore 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀/ for real-world implementations.   • Utilize 𝗝𝘂𝗽𝘆𝘁𝗲𝗿 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀 for fine-tuning and testing.      𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗧𝗶𝗽𝘀   ✔ Follow 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 to keep your codebase clean.   ✔ Write 𝘂𝗻𝗶𝘁 𝘁𝗲𝘀𝘁𝘀 for new components to ensure reliability.   ✔ Monitor 𝘁𝗼𝗸𝗲𝗻 𝘂𝘀𝗮𝗴𝗲 & 𝗔𝗣𝗜 𝗹𝗶𝗺𝗶𝘁𝘀 to optimize costs.   ✔ Keep 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 for easy scalability.  By adopting this structured approach, you can 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝘄𝗿𝗲𝘀𝘁𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻.  How do you structure your 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 projects? Share your thoughts in the comments!  

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 ]

One of the hardest parts of fine-tuning models? Getting high-quality data without breaching compliance. This Synthetic Data Generator Pipeline ia built to solve exactly that, and it is open-sources for you to use! You can now generate task-specific, high-quality synthetic datasets without using a single piece of real data, and still fine-tune performant models. Here’s what makes it different: → LLM-driven config generation Start with a simple prompt describing your task. The pipeline auto-generates YAMLs with structured I/O schemas, filters for diversity, and LLM-based evaluation criteria. → Streaming synthetic data generation The system emits JSON-formatted examples, prompt, response, metadata at scale. Each example includes row-level quality scores. You get transparency at both data and job level. → SFT + RFT with evaluator feedback We use models like DeepSeek R1 as judges. Low-quality clusters are automatically identified and regenerated. Each iteration teaches the model what “good” looks like. → Closed-loop optimization The pipeline fine-tunes itself, adjusting decoding params, enriching prompt structures, or expanding label schemas based on what’s missing. → Zero reliance on sensitive data No PII. No customer data. This is purpose-built for enterprise, healthcare, finance, and anyone who’s building responsibly. And it works: 📊 On an internal benchmark: - SFT with real, curated data: 79% accuracy - RFT with synthetic-only data: 73% accuracy That’s huge, especially when your hands are tied on data access. If you’re building copilots, vertical agents, or domain-specific models and want to skip the data wrangling phase, this is for you. Built by Fireworks AI 🔗 Try it out: https://lnkd.in/dXXDdyuM

Generating Synthetic Data: A Simple Guide Synthetic data generation is critical in building AI models where your data is sparse. Here we're not just filling gaps, we're carefully crafting data to make models smarter, more robust, and safer. Just like in cooking, you have different recipes for different goals. Here’s a simple breakdown of synthetic data generation methods 1. Generative Synthesis What it is: Letting a powerful AI (like a large language or image model) invent completely new examples from scratch based on a description or a set of rules. Good for: Generating massive amounts of novel data quickly. Watch out for: The AI can "hallucinate" and create nonsense or get stuck in a repetitive style. 2. Transformation & Rephrasing What it is: Taking existing, real data and altering it while keeping its core meaning. Think paraphrasing a sentence, swapping words, or changing an image's color. Good for: A cheap and safe way to make your dataset more diverse. Watch out for: Small changes can sometimes accidentally change the data's true label, so you need to double-check. 3. Programmatic Labeling & Distillation What it is: Using a smarter, more powerful AI (the "teacher") to label a bunch of unlabeled or messy data for a smaller, simpler AI (the "student") to learn from. Good for: Quickly creating labeled datasets at a huge scale. Watch out for: The student AI will inherit all the blind spots and biases of its teacher if you're not careful. 4. Agentic Self-Play What it is: Having multiple AI "agents" interact with each other or a simulated environment to create complex, multi-step data. This is perfect for generating conversations, tool-use sequences, or strategic game-play. Good for: Teaching AI how to perform long, complicated tasks. Watch out for: The AIs can learn to "cheat" the simulation or develop weird, unrealistic strategies that don't work in the real world. 5. Adversarial & Safety Data What it is: Intentionally creating tricky, confusing, or malicious data to find and fix your AI's weak spots. This is like a quality control check. Good for: Making your AI more robust, secure, and safe before it's deployed. Watch out for: You have to be very creative to think of all the ways things can go wrong. No matter which method you use, you need a strict quality control layer. This involves: Removing Duplicates: So the AI doesn't see the same example over and over. Scrubbing Sensitive Info: Filtering out personal data or offensive content. Tracking Lineage: Knowing exactly how and where a piece of synthetic data was created. The real thing isn't in any single technique, but in knowing which combination to use for your specific goal. Start simple, measure what works, and never compromise on data quality. After all, your AI will only ever be as good as the data it sees. #AI

Free 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 - steal it with pride! I have been developing Agentic Systems for around two years now. The same patterns keep emerging. Today, I am sharing my system of how to approach development of LLM based applications from idea to production. Let’s zoom in: 𝟭. Define a problem you want to solve: is GenAI even needed? 𝟮. Build a Prototype: figure out if the solution is feasible. 𝟯. Define Performance Metrics: you must have output metrics defined for how you will measure success of your application. 𝟰. Define Evals: split the above into smaller input metrics that can move the key metrics forward. Decompose them into tasks that could be automated and move the given input metrics. Define Evals for each. Store the Evals in your Observability Platform. ℹ️ Steps 𝟭. - 𝟰. are where AI Product Managers can help, but can also be handled by AI Engineers. 𝟱. Build a PoC: it can be simple (excel sheet) or more complex (user facing UI). Regardless of what it is, expose it to the users for feedback as soon as possible. 𝟲. Instrument your application: gather traces and human feedback and store it in an Observability Platform next to previously stored Evals. 𝟳. Run Evals on traced data: traces contain inputs and outputs of your application, run evals on top of them. 𝟴. Analyse Failing Evals and negative user feedback: this data is gold as it specifically pinpoints where the Agentic System needs improvement. 𝟵. Use data from the previous step to improve your application - prompt engineer, improve AI system topology, finetune models etc. Make sure that the changes move Evals into the right direction. 𝟭𝟬. Build and expose the improved application to the users. 𝟭𝟭. Monitor the application in production: this comes out of the box - you have implemented evaluations and traces for development purposes, they can be reused for monitoring. Configure specific alerting thresholds and enjoy the peace of mind. ✅ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: ➡️ Run steps 𝟲. - 𝟭𝟬. to continuously improve and evolve your application. ➡️ As you build up in complexity, new requirements can be added to the same application, this includes running steps 𝟭. - 𝟱. and attaching the new logic as routes to your Agentic System. ➡️ You start off with a simple Chatbot and add a route that can classify user queries to take action (e.g. add items to a shopping cart). I will be teaching how to apply this system hands-on and in detail as part of End-to-End AI Engineering Bootcamp (𝟭𝟬% 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁 𝗰𝗼𝗱𝗲: Kickoff10 ): https://lnkd.in/dGVhxAD9 What is your experience in evolving Agentic Systems? Let me know in the comments 👇 #LLM #AI #MachineLearning

What can #syntheticdata do to transform healthcare?   Data in biomedicine and healthcare often faces challenges - privacy concerns, data scarcity, biases, and underrepresentation of certain groups. Synthetic data, generated by #generativemodels, offers promising solutions.   In our #Nature Reviews Bioengineering paper, we explore the vast potential of synthetic data regarding:   #Privacy: Synthetic data mimics real data without exposing sensitive information. #Trust: Trust is crucial. Ensuring high-quality synthetic data requires robust evaluation methods to verify its accuracy and reliability. #DataScarcity: It fills gaps where real data is limited, enhancing AI models. #Fairness: By including underrepresented groups, it reduces bias and promotes more equitable healthcare. #ScenarioSimulation: Synthetic data helps simulate unseen scenarios for better testing.   The future of healthcare is data-driven, and synthetic data holds the potential to expand the possibilities of safer, more inclusive, and innovative care.   Find the paper here: https://lnkd.in/ehpQUErR   Boris van Breugel, Tennison Liu, Dino Oglic, AstraZeneca

If AI in your company still lives inside chat windows… you haven’t started the Agentic journey yet. Today’s Agentic AI systems don’t just answer questions. They observe signals, make decisions, trigger tools, coordinate workflows, and continuously improve outcomes. Instead of assisting humans one task at a time, these agents run end-to-end business operations across sales, support, finance, engineering, HR, and marketing. This is what production-grade Agentic AI actually looks like inside modern organizations: - Customer Support Agents Handle FAQs, resolve tickets, process refunds, update CRM systems, and escalate complex issues automatically. - Sales Ops Agents Qualify incoming leads, enrich prospect data, update pipelines, generate follow-ups, and notify sales teams in real time. - Marketing Automation Agents Plan campaigns, analyze audiences, generate content, schedule outreach, track performance, and optimize future runs. - Data Analysis Agents Convert business questions into SQL, clean datasets, analyze trends, generate insights, and deliver visual summaries. - Reporting Agents Pull metrics, validate data, create dashboards, write narratives, and distribute reports across stakeholders automatically. - QA / Testing Agents Generate test cases, execute regressions, detect failures, log bugs, and recommend fixes without manual intervention. - DevOps Agents Monitor infrastructure, detect anomalies, run diagnostics, apply rollbacks, notify teams, and assist deployments. - Finance Ops Agents Process invoices, categorize transactions, reconcile records, flag anomalies, and generate financial summaries. - HR Ops Agents Manage resume intake, screen candidates, schedule interviews, update HR systems, and respond to employee queries. - Research Agents Search documents and web sources, extract key findings, compare references, and summarize insights. - Content Creation Agents Outline topics, draft content, optimize for SEO and branding, publish assets, and track engagement end-to-end. - Internal Tools Agents Act as company copilots - understanding employee requests, calling internal APIs, executing actions, and confirming results. The real shift? These agents don’t just respond. They reason. They orchestrate tools. They execute workflows. They learn from feedback. They operate continuously. This is how organizations move from isolated automation to connected, outcome-driven AI systems. Not experiments. Not demos. Not pilots. Real production systems.

☕ Coffee Chats: Exploring AI Use Cases ☕ Welcome to another episode of Coffee Chats with Ranjani Mani and Vignesh Kumar. Today, we address a frequently asked question: "Where is AI being adopted, and what are the common use cases?" ⚙ Key Takeaways: 1. AI Adoption Levels:   - Basic: Common use cases like chatbots are evolving from heuristic to LLM-based models.   - Intermediate: Use cases such as multi-modality and text-to-SQL are gaining traction.   - Advanced: Cutting-edge scenarios like multi-agent environments are being experimented with. 2. Business Needs Focus:    - Productivity: Summarization, code generation, and conversational search.   - Automation: Supply chain processes, fraud detection, and customer journey automation.   - Customer Experience: Intelligent call centres, call centre agent assistance, and creative content generation. 3. Business Outcomes:   - New Revenue Streams: AI can identify new market opportunities and create innovative products or services, driving additional revenue. For example, AI-driven insights can uncover customer needs, leading to the development of targeted solutions.   - Differentiated Customer Experiences: AI enhances customer interactions by providing personalized and efficient services. Examples include AI-powered chatbots that offer real-time support, and recommendation systems that suggest products based on individual preferences.   - Modernizing Internal Processes: AI streamlines and optimizes internal operations, reducing costs and improving efficiency. Use cases include automating repetitive tasks, enhancing decision-making with predictive analytics, and improving supply chain management through real-time data analysis. 4. Evolving Use Cases:   - B2C vs. B2B: AI adoption varies between sectors. B2B use cases span manufacturing, healthcare, fintech, and more, while B2C focuses on creative applications like text-to-image and text-to-video. AI adoption is high in areas with low-hanging fruits, such as language translation and customer service, offering immediate benefits like improved service quality and capacity. Additionally, AI is solving complex problems in areas like drug discovery and space technology, accelerating innovation. Optimizing for low-risk use cases, especially in data privacy-sensitive industries, is crucial. The AI landscape is evolving rapidly, and we will continue to monitor and explore these developments. 💬 If you have other examples or topics you'd love to share, please drop us a note in the comments or send us a message! #AI #ArtificialIntelligence #TechInnovation #BusinessTransformation #AIUseCases #Productivity #Automation #CustomerExperience

You cannot train AI on reality alone anymore. There is not enough of it. Jensen Huang explains why NVIDIA built Cosmos, an AI world model that generates synthetic training data grounded in physics. The problem is simple. Teaching physical AI like robotics requires vast amounts of diverse interaction data. Videos exist, but not nearly enough to capture the variety of situations robots will encounter. So NVIDIA transformed compute into data. Using synthetic data generation grounded by laws of physics, they can selectively generate training scenarios that would be impossible to capture otherwise. The example Huang shows is remarkable. A basic traffic simulator output gets fed into Cosmos. What emerges is physically plausible surround video that AI can learn from. This solves a fundamental limitation. You cannot train autonomous systems on every possible scenario by recording reality. There are not enough cameras or time. But you can simulate physics accurately enough that AI trained on synthetic data generalises to real environments. This applies beyond robotics. Any AI learning physical interactions, from manufacturing to logistics to infrastructure monitoring, faces the same data scarcity problem. Synthetic data generation grounded in physics laws is how you create training sets reality cannot provide. The organisations building AI for physical systems will either master synthetic data generation or get limited by whatever reality they can record. Watch the full presentation to hear Huang explain how Cosmos generates training data for physical AI. What physical AI application needs synthetic data because reality cannot provide enough examples? #AI #SyntheticData #Robotics #NVIDIA #MachineLearning

Most people want to use Generative AI. Fewer know how to build it. Even fewer know how to build it right. That’s where a roadmap like this becomes essential. I just went through this detailed Generative AI Roadmap, and it lays out a learning path from fundamentals all the way to deploying AI agents and real-world apps. If you're serious about building GenAI skills, here’s what’s included: - Start with core concepts: supervised vs. unsupervised learning, overfitting, basic Python, matrix ops, probability - Move into generative modeling: RNNs, autoencoders, latent space, backprop, VAEs - Deep dive into GANs & diffusion models: StyleGAN, CycleGAN, Stable Diffusion, U-Nets - Explore LLMs for text generation: transformers, attention, prompt engineering, few-shot learning - Go beyond text: music, audio, synthetic data, 3D generation - Learn fine-tuning techniques: LoRA, PEFT, instruction tuning - Then get hands-on with deployment: containerization, quantization, APIs, scaling - And finally, build AI agents with LangChain, CrewAI, and n8n—tying perception, reasoning, and action into workflows This roadmap is perfect for developers, ML engineers, and even product teams looking to understand what it really takes to go from an idea to a working GenAI app. -- Join our Newsletter with 137K Subscribers — www.theravitshow.com