Brij kishore Pandey's Linkedin Analytics

We are entering a phase where 𝘬𝘯𝘰𝘸𝘪𝘯𝘨 AI isn’t enough — 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 and 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 powerful, responsible AI systems will set you apart. To help navigate this rapidly evolving landscape, here’s a structured 𝟵-𝘀𝘁𝗮𝗴𝗲 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 to mastering Generative AI in 2025: → 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜: Understand the real differences between AI, ML, and DL. Master the fundamentals like optimizers, activation functions, and gradient descent. → 𝗗𝗮𝘁𝗮 & 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: High-performing AI starts with high-quality data. Learn how to clean, normalize, tokenize, engineer features, and balance datasets for better model accuracy. → 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀): Go deeper than just using GPTs. Study how transformers work, what positional encoding means, and how scaling laws govern large models. → 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Learn how to design effective prompts, create structured prompt chains, manage token budgets, and optimize model outputs systematically. → 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 & 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: Master advanced techniques like PEFT, LoRA, and RLHF to fine-tune and optimize models with minimal data and efficient resource usage. → 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 & 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: Expand beyond text to images, audio, video, and cross-modal generation. Understand diffusion models, captioning, and multimodal search. → 𝗥𝗔𝗚 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Learn how retrieval-augmented generation (RAG) systems ground models with external knowledge. Explore vector databases like Pinecone, ChromaDB, and FAISS. → 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 & 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: Identify biases, ensure transparency, and integrate responsible AI practices into your systems — because trust and accountability are not optional. → 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲: Turn prototypes into production-grade systems. Focus on API serving, scaling, inference optimization, logging, and setting usage controls. Each stage is mapped with the most relevant 𝘁𝗼𝗼𝗹𝘀, 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀, and 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 to focus on. The world does not just need more AI models. It needs 𝗯𝗲𝘁𝘁𝗲𝗿, 𝘀𝗮𝗳𝗲𝗿, and 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱-𝗿𝗲𝗮𝗱𝘆 AI systems. Built by those who deeply understand the full lifecycle from idea to deployment. → 𝗦𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗿𝗼𝗮𝗱𝗺𝗮𝗽. → 𝗥𝗲𝗳𝗹𝗲𝗰𝘁 𝗼𝗻 𝗶𝘁. → 𝗨𝘀𝗲 𝗶𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗺𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗮𝗻𝗱 𝗯𝗲𝘆𝗼𝗻𝗱.

When working with multiple LLM providers, managing prompts, and handling complex data flows — structure isn't a luxury, it's a necessity. A well-organized architecture enables: → Collaboration between ML engineers and developers → Rapid experimentation with reproducibility → Consistent error handling, rate limiting, and logging → Clear separation of configuration (YAML) and logic (code) 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗧𝗵𝗮𝘁 𝗗𝗿𝗶𝘃𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 It’s not just about folder layout — it’s how components interact and scale together: → Centralized configuration using YAML files → A dedicated prompt engineering module with templates and few-shot examples → Properly sandboxed model clients with standardized interfaces → Utilities for caching, observability, and structured logging → Modular handlers for managing API calls and workflows This setup can save teams countless hours in debugging, onboarding, and scaling real-world GenAI systems — whether you're building RAG pipelines, fine-tuning models, or developing agent-based architectures. → What’s your go-to project structure when working with LLMs or Generative AI systems? Let’s share ideas and learn from each other.

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗶𝘀 𝗛𝗲𝗿𝗲 - 𝗮𝗻𝗱 𝗜𝘁’𝘀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰, 𝗢𝗽𝗲𝗻, 𝗮𝗻𝗱 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 Day 2 at IBM   #Think2025 made one thing clear: we’ve entered the Agent Era. This isn’t just about conversational AI anymore - these agents are capable of taking real, contextual action across complex workflows. 𝗙𝗿𝗼𝗺 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝘁𝗼 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 IBM watsonx Orchestrate is helping enterprises move from passive assistance to active orchestration. These agents are designed to think, plan, act, and reflect. They’re not built for demos - they’re built for work. What they can do: ↑ Automate full workflows, like onboarding a client in minutes instead of 24 hours  ↑ Coordinate across systems like Salesforce, SAP Ariba, and Dun & Bradstreet  ↑ Use business context and behavioral logic to make decisions in real time One example stood out: A single Slack command triggered multiple agents - a customer verification agent, a legal agent, a company profiler, and an onboarding agent - to work together and onboard a client called “Focus Corp” within seconds. That’s not just integration; that’s orchestration in action. 𝗪𝗵𝗲𝗿𝗲 𝗔𝗜, 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲 Watsonx sits at the intersection of four major enterprise priorities: • Intelligent process automation  • Conversational interfaces  • Autonomous AI agents  • Orchestration across any AI model or enterprise tool     What stood out is the level of composability. You can combine: • Pre-built agents for HR, procurement, sales  • A no-code builder for custom agents  • Domain-specific tools and connectors  • All backed by enterprise-grade security, governance, and explainability     𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗶𝗺𝗽𝗮𝗰𝘁  This isn’t theory. We saw examples from: ↑ The Port of Barcelona: reducing carbon footprint and system latency through AI-led infrastructure ↑ BNP Paribas: building secure, hybrid cloud environments with embedded AI ↑ Fiserv: automating HR interactions with over 20,000 employee queries handled without escalation  • 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Enterprise AI is no longer experimental. It’s operational. It’s composable. And it’s already changing how work gets done. If you're exploring how agentic AI could reshape your workflows, architecture, or internal tools - now’s the time to dig deeper. Learn more here: https://lnkd.in/egNCSD5M #IBMPartner

If you’re serious about learning or building in Agentic AI space, here’s a curated collection of free courses, hands-on frameworks, and real-world resources to guide your journey: 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 → IBM’s introduction to Agentic AI https://lnkd.in/eWQCS9Nu → Aisera’s take on Agentic AI trends in 2025 https://lnkd.in/e698yjjH → Coursera  – “𝘈𝘨𝘦𝘯𝘵𝘪𝘤 𝘈𝘐 𝘢𝘯𝘥 𝘈𝘐 𝘈𝘨𝘦𝘯𝘵𝘴 𝘧𝘰𝘳 𝘓𝘦𝘢𝘥𝘦𝘳𝘴” https://lnkd.in/e9ew9tDj → Coursera  – “𝘈𝘐 𝘈𝘨𝘦𝘯𝘵𝘴 𝘢𝘯𝘥 𝘈𝘨𝘦𝘯𝘵𝘪𝘤 𝘈𝘐 𝘸𝘪𝘵𝘩 𝘗𝘺𝘵𝘩𝘰𝘯 & 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘈𝘐” https://lnkd.in/eSd39Apj → Microsoft GitHub course – 𝘈𝘐 𝘈𝘨𝘦𝘯𝘵𝘴 𝘧𝘰𝘳 𝘉𝘦𝘨𝘪𝘯𝘯𝘦𝘳𝘴 https://lnkd.in/eZeXXee5 → Reddit roadmaps https://lnkd.in/exjamc5w https://lnkd.in/e-HiCyQq 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 → (Python) – Start building with LLMs https://lnkd.in/eVeTuPEg → Build collaborative multi-agent systems https://docs.crewai.com/ → Multi-agent orchestration, AutoGen Bench & Studio https://lnkd.in/ePgaRvKF → Graph-based agent workflows built on LangChain https://lnkd.in/emh5_UDP → LlamaIndex – Knowledge retrieval for agents (RAG) https://lnkd.in/ejvN2u5d → SDK to integrate AI into traditional software https://lnkd.in/eVaBP73X → Agentic-AI -Tutorials, blogs, projects https://lnkd.in/ecd-5udC 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘆𝗼𝘂𝗿 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁. Bookmark this. Build something. Iterate.