Sanjeev Singh

If you are into GenAI, you must know these 12 Terms - 1. LLM (Large Language Model) - What: Advanced AI systems trained on massive text datasets - Used in: ChatGPT, Claude, Gemini, Copilot - Real-world example: GitHub Copilot using LLMs for code suggestions 2. Transformers - What: Neural network architecture that revolutionized AI - Used in: All modern language models (BERT, GPT, T5) - Real-world example: Google BERT powering search results understanding 3. Prompt Engineering - What: Art of crafting effective instructions for AI models - Used in: • Business applications • Content creation • Specialized tasks - Real-world example: Writing prompts for image generation in DALL-E 4. Fine-tuning - What: Customizing pre-trained models for specific tasks - Used in: • Industry-specific applications • Custom chatbots • Specialized tools - Real-world example: Fine-tuning models for medical diagnosis 5. Embeddings - What: Numerical representations of text/images - Used in: • Search engines • Recommendation systems • Document comparison - Real-world example: Pinecone using embeddings for vector search 6. RAG (Retrieval Augmented Generation) - What: Combining external knowledge with AI generation - Used in: • Enterprise chatbots • Documentation tools • Customer support - Real-world example: Enterprise chatbots accessing company documentation 7. Tokens - What: Units of text processing in AI models - Used in: • Model capacity planning • Cost calculation • Input/output management - Real-world example: GPT-4's 32k token context window 8. Hallucination - What: AI generating false but plausible information - Impact on: • Business applications • Content generation • Decision support - Real-world example: ChatGPT generating incorrect historical dates 9. Zero-shot Learning - What: AI performing tasks without specific training - Used in: • Classification tasks • Language understanding • New use cases - Real-world example: Classifying new product categories without training 10. Chain-of-Thought - What: Step-by-step reasoning in AI responses - Used in: • Problem solving • Mathematical calculations • Logic tasks - Real-world example: Solving complex math problems step-by-step 11. Context Window - What: Maximum text length AI can process - Impact on: • Document processing • Conversation length • Task complexity - Real-world example: Processing long legal documents 12. Temperature - What: Controls AI output randomness - Used in: • Creative writing • Code generation • Response variation - Settings: • Low (0.0): Consistent, focused • High (0.7-1.0): Creative, varied - Real-world example: Adjusting creativity in marketing copy generation What other GenAI terms do you use regularly? Please share them below, and I will add them in the next!

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🚀 How to Evaluate LLM Applications Large Language Models (LLMs) are powerful, but their real impact depends on how we evaluate them. The right benchmarks and evaluation methods ensure accuracy, fairness, and real-world applicability. 🔍 Key Takeaways from the Framework: ✅ Benchmarks Language Understanding & QA: TruthfulQA, MMLU, DROP Common-sense & Reasoning: ARC, HellaSwag, BIG-Bench Hard Coding: HumanEval, CodeXGLUE Conversation & Chatbots: Chatbot Arena, MT Bench, Stanford HELM ✅ Evaluation Methods Character Level: Levenshtein Distance Word Based: BLEU, ROUGE, METEOR, Perplexity Embedding Based: BERTScore, MoverScore, QAG Score Language Model Based: GPTScore, SelfCheckGPT, G-Eval, Prometheus NLP Models: NLI, BLEURT Frameworks: DeepEval, RAGAS 💡 Whether you’re building chatbots, coding assistants, or reasoning systems, choosing the right benchmark + evaluation method is crucial to measure performance and reliability. 👉 Question for you: Which evaluation method do you think gives the most real-world reflection of LLM performance traditional metrics like BLEU/ROUGE or LLM-based evaluations like GPTScore & SelfCheckGPT? #LLM #AI #MachineLearning #NLP #Evaluation #GenAI #ArtificialIntelligence

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The Microsoft–OpenAI 2030 deal isn’t just tech drama. It’s a signal flare for every consulting and IT services firm. Here’s the TL;DR: OpenAI wants to cut Microsoft’s revenue share in half by 2030. Translation? More multi-cloud AI, more model choice, and a lot less vendor lock-in. For consulting giants like Capgemini, Accenture, and Deloitte, this is both a threat and a jackpot: • Threat: Microsoft will push harder on its own Copilot stack, locking in customers before OpenAI opens the gates. • Jackpot: Enterprises will demand model-agnostic, cloud-portable AI architectures—and they’ll pay a premium for the escape hatch. Winners by 2030 will be the ones who: 1. Build model-agnostic AI factories that can swap OpenAI, Anthropic, Mistral, Gemini, Llama without rewriting the app. 2. Marry compliance + sovereignty with AI (think EU AI Act baked into every deployment). 3. Master the economics—balancing performance, cost, and portability across clouds. The punchline? The 2030 revenue cut isn’t about Microsoft or OpenAI. It’s about who owns the customer relationship when the AI stack is truly open. #capgemini #genai

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🚀 Understanding the Evolution of LLM Architectures: MCP vs Agentic AI vs RAG As enterprises rapidly adopt AI, it’s important to evaluate how models interact with data and execute tasks. Here's a quick breakdown of three leading approaches: 🟣 Model Context Protocol (MCP): A standardized protocol ensuring direct, real-time, secure access to dynamic data sources—without relying on vector storage. Ideal for low-latency enterprise applications. 🟧 Agentic AI: Multiple intelligent agents coordinate via an orchestrator (or crew system). Offers flexibility but brings high latency, resource intensiveness, and debugging challenges. 🩷 Retrieval Augmented Generation (RAG): Fetches context via embeddings from vector DBs before response generation. Powerful but suffers from static embeddings, semantic gaps, and context staleness. 💡 Each architecture has trade-offs. The future may not be “one size fits all” but a hybrid model that combines the best of each. Which architecture do you think will dominate in enterprise AI adoption

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We compared self-hosted and API-based LLM deployments across a range of models, including quantized variants. One of the key factors that emerged was the role of utilization in determining cost-efficiency. Even with FP8 quantization and tuned hardware, the self-hosted environments didn’t come close to matching API efficiency. The gap wasn’t due to performance—latency and throughput were strong when the systems were busy. The challenge was keeping the GPUs consistently utilized. When utilization dropped, unit costs increased significantly. The API-based models were generally stable over the 7-day window. However, we did observe some short periods of degraded performance—spikes in latency and changes in throughput. These weren’t persistent, but they highlight a potential challenge in scenarios where predictable response times are required. Overall, the testing reinforced that deployment tradeoffs depend heavily on workload characteristics. Sustained, high-throughput demand makes it easier to justify self-hosting. For more variable or bursty workloads, efficiency is harder to maintain without advanced orchestration. Full research note is available here: https://lnkd.in/eeCY2Z5R

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Model Context Protocol (MCP): The Next Frontier for AI Product Managers As AI systems evolve from static models to dynamic, tool-augmented agents, one protocol is redefining how these intelligent systems interact — Model Context Protocol (MCP). 🔍 What is MCP? MCP is an open standard that enables AI models to communicate securely with external tools, APIs, and data sources — allowing for richer, more controlled, and context-aware reasoning. 💡 Why It Matters for AI Product Managers ◾ Interoperability: Enables your AI systems to integrate across diverse applications seamlessly. ◾Security & Governance: Offers transparency and controlled access to external systems. ◾Scalability: Supports multi-agent orchestration and safe tool usage at enterprise scale. ⚙️ The AI PM’s Opportunity ◾For AI Product Managers, MCP isn’t just a protocol — it’s a design principle for building next-gen intelligent ecosystems. It bridges the gap between foundation models and enterprise applications, ensuring reliability, compliance, and trust. ◾As tool-using AI agents become the norm, understanding MCP will soon be as critical as knowing APIs or SDKs. #AIProductManagement #ModelContextProtocol #MCP #AIProductManager #MultiAgentSystems #AIAgents #AIIntegration #AIGovernance #LangChain #Claude #Gemini #OpenAI #AIProductDesign

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𝐀 𝐝𝐞𝐞𝐩 𝐝𝐫𝐢𝐯𝐞 𝐨𝐧 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 🚀 LLMs are generative, meaning that they produce meaningful, coherent text in a sequential manner based on a user prompt. However, with LLMs there were some inherent drawbacks 👉Concurrent hallucinations on the responses 👉Information is always “lost” in a model’s internal representation of the data 👉LLM cannot know facts that occurred after its training was completed These are addressed through VectorDB that seeds data as "vectors" and functions as an underlying #storage layer that can be efficiently queried by an LLM to retrieve facts But how does it work under the hood 🤔 ✅ A user sends a query via an application’s UI to the embedding model, which converts the input query to a vector ✅ The data that sits somewhere (locally or on the cloud) is passed to an embedding model. ✅ It is converted to vector form and the storage layer of a vector DB via the API gateway ✅ The data is indexed, during which it’s partitioned/sharded for scalability and faster lookups ✅ The query engine is tightly integrated with the storage layer, to allow for rapid retrieval of nearest neighbours via the database’s ANN (Appropriate Nearest Neighbour) implementation ✅ The vectorized query is sent to the query engine via the API gateway that can handle async queries at nth time ✅ The response from the query engine is returned back with top-k results back to the user. Vector databases are incredibly powerful, but has its own limitation such as fitting the query to the embedding sequence and also some search results can get omitted due to incorrect "paraphase mismatch" What are your thoughts on vectorDB, share your comments below 👇 #genai #database #ai #agents #dataschema #platform #rag #llm

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Architectural Intelligence: Accelerating solutions in the age of AI With AI copilots, solutions are being built faster than ever. But speed without structure leads to extreme risk. Manual governance can’t keep up. Today’s pace demands automated, embedded, intelligent governance that: - Capture solution intent through guided intake - Map it to approved technology standards (core and exceptions), approved reference architectures and design patterns - Run automated governance checks for compliance, scalability, and security - to remove single points of failure - Flag deviations and suggest remediations - Generate architecture diagrams based on Well Architected Framework approach and governance reports—FAST This isn’t just automation—it’s architectural intelligence. A way to embed standards, patterns, and best practices into every build, without slowing down delivery. As our engineers use AI agents to accelerate the creation of apps, and workflows, we need systems that govern at scale—without manual bottlenecks. This approach makes governance a built-in accelerator, not just a guardrail. #ArchitectureIntelligence #GovernanceByDesign #AgenticAI #EnterpriseArchitecture

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The Fine-Tuning Dictionary 2025 Edition Confused by all the terms around AI fine-tuning? I built a simple, practical dictionary that explains core concepts used by ML engineers, AI builders, and researchers. From: → Batch Size vs Learning Rate → Pretraining vs Fine-Tuning → Embeddings, Gradient Descent, Regularization, Tokenization → And even newer ideas like Joint Learning and Yield Optimization This isn’t just for academics. It’s for anyone customizing AI models, product teams, startups, and builders using LLMs. Each term is defined in 1–2 sentences, plain English, no fluff. Practical enough to help you explain or implement with confidence. Follow the AIKaDoctor (Free AI & Data Science Resources) channel on WhatsApp: https://lnkd.in/dCTCEKKc Follow Habib Shaikh For more such content.

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SandLogic models span a spectrum - from open-source foundations to fully in-house innovations, each with its own role in the ecosystem of intelligence. 🔹 Lexicons – curated open-source models, quantized and made accessible for enterprises and developers. 🔹 Nexons – enhanced models, where we take strong open foundations and fine-tune them with our own datasets to bring sharper performance, trust, and relevance. (We’ll be releasing our first Nexon very soon.) 🔹 Shakti – our fully in-house series of Small & Mid Language Models, built ground-up for edge and enterprise AI. Together, they form a continuum of intelligence: from open knowledge (Lexicons), to refined bridges (Nexons), to original innovation (Shakti). Our vision is to make AI accessible, reliable, and meaningful - whether it runs on the cloud, on-premise, or at the edge. #AI #LLMs #EdgeAI #SandLogic #Innovation #slms #genai #generativeai #opensourceslms #nativellms #nativeslms

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Handoffs are SO critical when deploying Enterprise AI stacks >> 4 situations + examples:  1. Data to Model. Your model is only as good as the data it receives. If upstream data is messy, late, or stripped of context, the model produces “good-looking” but useless output. → A Bank’s KYC AI failed because PDFs weren’t standardised. So humans ended up retyping half the forms. 2. Model to Agent. Models are reasoning engines; agents execute workflows. When the transition is weak, such as with unclear prompts, no fallback logic, etc., the agent spins its wheels. → A telco’s chatbot ran on GPT-4 but kept looping because the agent layer had no escalation logic. 3. Agent to System (of Records/ Engagements). Most enterprises don’t replace core systems. They layer AI on top. If agents can’t push validated outputs into ERP, CRM, or ticketing tools cleanly, human rework wipes out the benefit. → A logistics firm shaved minutes off routing with agents, but lost hours re-entering data into SAP. 4. Team to Team. The most overlooked handoff. If AI-augmented teams don’t share their workflows across functions, adoption stalls. Silent optimisers create personal wins; wallflowers slow projects. → At one insurer, claims adjusters with AI tools closed claims in 2 days, but legal still worked at 12 days. The gap killed ROI. The pattern is clear: the handoff is where productivity lives or dies. And the second-order effect? Ownership of the handoff is becoming more strategic than ownership of the tool. CIOs are beginning to treat orchestration as a board-level capability, because it’s where cost, speed, and compliance collide. Enterprise AI won’t fail because the models don’t think. It’ll fail if the stack can’t pass the baton. → Where are the weakest handoffs in your stack — data, agent, system, or team? → I share tactical views and advice on real-world enterprise-AI battles. If that’s your arena, hit Follow and catch my Thursday deep-dive newsletter right here on LinkedIn.

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D&A architects must integrate and scale GenAI and LLM solutions while ensuring data literacy, governance, and trust across diverse deployment methods: https://gtnr.it/48BzKUD Gartner for Technical Professionals provides in-depth research tailored to IT professionals and architects implementing technical strategies. Gartner for IT | #GartnerIT #AI #Leadership

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🚀From AIOps → LLMOps → LMOps → and now: AgentOps AI operations have evolved step by step: AIOps automated IT operations. LMOps managed the ML lifecycle. LLMOps focused on deploying and governing large language models. But we’re now entering the era of multi-agent systems ,where multiple autonomous AI agents collaborate, negotiate, and act together. Managing this shift requires a new discipline: AgentOps. 🔄 The AgentOps Lifecycle AgentOps ensures that agent ecosystems are not only deployed but remain safe, scalable, and outcome-driven through: Plan & Orchestrate – design agent roles, goals, and workflows. Configure Policies & Guardrails – enforce safety, compliance, and alignment. Deploy Multi-Agent Workflows – enable coordination across agents and humans. Monitor & Observe – track decisions, detect drift, and ensure reliability. Evaluate & Optimize – measure ROI, refine strategies, and continuously improve. Why this matters for enterprises Coordination: Agents don’t just execute tasks — they collaborate, which introduces new complexities. Risk: Unchecked autonomy creates exposure in compliance, bias, and security. ROI: Proper AgentOps enables safe scaling of multi-agent ecosystems, unlocking productivity gains across workflows. AgentOps is to AI agents what DevOps was to software: the discipline that makes innovation scalable, repeatable, and trustworthy.

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