Key Elements of AI

𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗮𝘁 𝗔𝗜 𝗶𝘀 𝗮 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁 𝗽𝗮𝘁𝗵 𝗳𝗿𝗼𝗺 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘃𝗮𝗹𝘂𝗲. The assumption: 𝗗𝗮𝘁𝗮 → 𝗔I → 𝗩𝗮𝗹𝘂𝗲 But in real-world enterprise settings, the process is significantly more complex, requiring multiple layers of engineering, science, and governance. Here’s what it actually takes: 𝗗𝗮𝘁𝗮 • Begins with selection, sourcing, and synthesis. The quality, consistency, and context of the data directly impact the model’s performance. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 • 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Exploration, cleaning, normalization, and feature engineering are critical before modeling begins. These steps form the foundation of every AI workflow. • 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: This includes model selection, training, evaluation, and tuning. Without rigorous evaluation, even the best algorithms will fail to generalize. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • Getting models into production requires deployment, monitoring, and retraining. This is where many teams struggle—moving from prototype to production-grade systems that scale. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 • Legal regulations, ethical transparency, historical bias, and security concerns aren’t optional. They shape architecture, workflows, and responsibilities from the ground up. 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰. 𝗜𝘁’𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗿𝗶𝗴𝗼𝗿 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗮𝘁𝘂𝗿𝗶𝘁𝘆. Understanding this distinction is the first step toward building AI systems that are responsible, sustainable, and capable of delivering long-term value.

AI Engineering has four levels to it! – Level 1: Using AI Start by mastering the fundamentals: -- Prompt engineering (zero-shot, few-shot, chain-of-thought) -- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face) -- Understanding tokens, context windows, and parameters (temperature, top-p) With just these basics, you can already solve real problems. – Level 2: Integrating AI Move from using AI to building with it: -- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus) -- Embeddings and similarity search (cosine, Euclidean, dot product) -- Caching and batching for cost and latency improvements -- Agents and tool use (safe function calling, API orchestration) This is the foundation of most modern AI products. – Level 3: Engineering AI Systems Level up from prototypes to production-ready systems: -- Fine-tuning vs instruction-tuning vs RLHF (know when each applies) -- Guardrails for safety and compliance (filters, validators, adversarial testing) -- Multi-model architectures (LLMs + smaller specialized models) -- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals) Here’s where you shift from “it works” to “it works reliably.” – Level 4: Optimizing AI at Scale Finally, learn how to run AI systems efficiently and responsibly: -- Distributed inference (vLLM, Ray Serve, Hugging Face TGI) -- Managing context length and memory (chunking, summarization, attention strategies) -- Balancing cost vs performance (open-source vs proprietary tradeoffs) -- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR) At this stage, you’re not just building AI—you’re designing systems that scale in the real world. What else would you add? Subscribe to my free blog for more learning blog.dataexpert.io

AI Engineering ≠ SW Engineering. Nor is it ML Engineering. Let’s stop the confusion once and for all. As an engineering manager, here’s what I see most engineers get wrong: not understanding what AI engineering truly looks like Let me give you solid, day-to-day examples: 1. Need a new feature? ⥽SWE: You scope out requirements, design a system, and write every line of logic yourself. ⥽AI Engineer: You find an existing AI model (say, GPT-5 or Gemini), and adapt it with prompts or lightweight fine-tuning to your use case. 2. When a business user asks, “Can we automate this?” ⥽SWE: You look for APIs, build custom rules, and code the workflow. ⥽AI Engineer: You ask, “Can an LLM or a vision model do 80% of this out-of-the-box?” If yes, you integrate, not re-invent. 3. Improving a search bar ⥽SWE: Optimize string matching, maybe build autocomplete from scratch. ⥽AI Engineer: Plug in embeddings from a pre-trained model for semantic search, no need to build new logic. 4. Document processing ⥽SWE: Regex, manual parsers, edge case handling. ⥽AI Engineer: Use an OCR + LLM pipeline, add guardrails to catch model hallucinations. 5. Product QA ⥽SWE: You test edge cases, business logic, inputs/outputs, and deterministic. ⥽AI Engineer: You test probabilistic outputs, run prompt variation tests, evaluate with real user data, and watch for bias/errors you can’t predict. 6. Release cycles ⥽SWE: Every change means a code update, deployment, and regression testing. ⥽AI Engineer: Sometimes, you just update a prompt or swap a model version, no full redeploy. 7. User feedback loop ⥽SWE: Feedback = bug report, fix the function, redeploy. ⥽AI Engineer: Feedback = adjust prompt, tweak the model, retrain, or even switch APIs. 8. Security ⥽SWE: Input sanitization, XSS/SQL injection checks, and access controls. ⥽AI Engineer: Prompt injection protection, controlling model responses, data redaction before sending to APIs. 9. Scaling ⥽SWE: Optimize backend, add load balancers, scale microservices. ⥽AI Engineer: Optimize model API usage, cache responses, batch queries to control token cost. 10. Hiring & skills ⥽SWE: Look for CS fundamentals, data structures, algorithms, OOP. ⥽AI Engineer: Look for prompt design, LLM adaptation, model evaluation, and rapid prototyping with AI APIs. Bottom line: → Software Engineers build logic from scratch. → ML Engineers train models from scratch. → AI Engineers build products with models already trained. The best combination is having solid fundamentals as a software engineer and then combining it with AI, so you can go beyond what it can do for you and give quality output.

If you are an AI engineer, thinking how to choose the right foundational model, this one is for you 👇 Whether you’re building an internal AI assistant, a document summarization tool, or real-time analytics workflows, the model you pick will shape performance, cost, governance, and trust. Here’s a distilled framework that’s been helping me and many teams navigate this: 1. Start with your use case, then work backwards. Craft your ideal prompt + answer combo first. Reverse-engineer what knowledge and behavior is needed. Ask: → What are the real prompts my team will use? → Are these retrieval-heavy, multilingual, highly specific, or fast-response tasks? → Can I break down the use case into reusable prompt patterns? 2. Right-size the model. Bigger isn’t always better. A 70B parameter model may sound tempting, but an 8B specialized one could deliver comparable output, faster and cheaper, when paired with: → Prompt tuning → RAG (Retrieval-Augmented Generation) → Instruction tuning via InstructLab Try the best first, but always test if a smaller one can be tuned to reach the same quality. 3. Evaluate performance across three dimensions: → Accuracy: Use the right metric (BLEU, ROUGE, perplexity). → Reliability: Look for transparency into training data, consistency across inputs, and reduced hallucinations. → Speed: Does your use case need instant answers (chatbots, fraud detection) or precise outputs (financial forecasts)? 4. Factor in governance and risk Prioritize models that: → Offer training traceability and explainability → Align with your organization’s risk posture → Allow you to monitor for privacy, bias, and toxicity Responsible deployment begins with responsible selection. 5. Balance performance, deployment, and ROI Think about: → Total cost of ownership (TCO) → Where and how you’ll deploy (on-prem, hybrid, or cloud) → If smaller models reduce GPU costs while meeting performance Also, keep your ESG goals in mind, lighter models can be greener too. 6. The model selection process isn’t linear, it’s cyclical. Revisit the decision as new models emerge, use cases evolve, or infra constraints shift. Governance isn’t a checklist, it’s a continuous layer. My 2 cents 🫰 You don’t need one perfect model. You need the right mix of models, tuned, tested, and aligned with your org’s AI maturity and business priorities. ------------ If you found this insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content ❤️

The biggest myth in AI today? That tools like LLMs, CoPilots, MCPs, and Agents will do the engineering for you. They won’t — because AI is engineering. LLMs. MCP. Agents. They’re all just that — tools. Yet many organizations are spending an extraordinary amount of time comparing, evaluating, and switching between tools — while missing the real essence of AI transformation. The real differentiator isn’t the toolchain. It’s the engineering mindset behind how those tools are used. Most organizations miss that AI is an engineering discipline — not a collection of experiments. It demands the same rigor as any mature system: design, development, testing, validation, rollout, and continuous optimization. Don’t go by leaderboards — they’re tested to work in controlled benchmarks, not in real-world, multi-system environments where context, latency, data, and cost all collide. And don’t fall for the misconception that AI will replace engineers. That’s a narrative being set — but having worked with top LLMs and chatbots, one thing is clear: they often fail when confronted with real engineering. Their code lacks depth, structure, and holistic system thinking. Tools never replace real engineering. They amplify those who understand it. Invest in the core. Invest in robust engineering practices. Upskill your teams. This will be your foundation in building scalable, responsible, and future-ready AI systems. Because tools will change. Frameworks will evolve. But engineering excellence — that’s what endures #aiengineering #ai #leanagenticai

I’m in board rooms and executive sessions witnessing AI strategies fall into 3 traps: 1. Too vague (“We need to be more innovative.”) 2. Too detailed (30 page deck with 50 slides in the appendix that no one reads) 3. Too disconnected (Misaligned with actual capabilities) If your AI strategy has more slides than decisions, you might be confusing activity with alignment. The result? ✔️An AI strategy that costs $1M and 75% of the use cases aren’t even executable . ✔️A transformation roadmap that spans 5 years, but no one knows what to do next quarter. AI is not just a tool. It’s a force that can reshape your workflows, redefine roles, and reallocate talent. Without a clear strategy, you’ll fall into two traps: 🤯FOMO-driven chaos: Buying licenses ≠ transformation. 🤯Pilot purgatory: Endless experimentation without scale. But here’s the truth: You don’t need a fancier strategy. You need a functional one. What a Good AI Strategy Actually Needs: 🧭 Clarity – What problem are you solving? – Why AI, not automation or process reengineering? ⚙️ Capability Mapping – Do you have the data? – Do you have the people? – Do you have the infrastructure? 📆 Time-Boxed Roadmap – What’s your “Crawl → Walk → Run” plan over the next 3, 6, 12 months? – How are you measuring success at each step? If your AI strategy doesn’t clearly answer those questions… it’s not a strategy. It’s a slide deck! Sol’s Recommendations: 1️⃣ Think Big. Start Small. Scale Smart. A good strategy should fit on one slide. It should move people to act, not stall them in analysis. 2️⃣ Build Feedback Loops INTO the Strategy Strategy isn’t a map—it’s a GPS. It must update as the terrain shifts. That means monthly retros, live dashboards, and real business input—not just consulting jargon. 3️⃣ Don’t confuse motion with momentum. Start small, but make sure it moves the needle. 4️⃣ Map readiness before roadmap. Strategy isn’t just about what you want to do, it’s about what you’re equipped to do now and how fast you can scale. Great AI strategy isn’t built on use cases but also use-case readiness! What’s the worst strategy deck you’ve ever seen? Drop your horror stories (or recovery stories) below. I’m all ears. #Strategy #Execution #FutureOfWork #AILeadership #DigitalTransformation #SolRashidi #RealTalkStrategy #AI #Automation #Agents #AIstrategy #humanresources

Too many AI strategies are being built around the technology instead of the business challenges they should solve. The real value of AI comes when it is directly tied to your goals. I have arrived at seven lessons on how to align your AI strategy directly with your business goals: 1. Start with the "why," not the "what." Before discussing models or tools, ask what business problem you need to solve. It could be speeding up product development, or cutting operational costs. Let that answer be your guide. 2. Think in terms of business outcomes. Measure AI success by its impact on metrics like revenue growth or employee productivity not by technical accuracy. 3. Build a cross-functional team. AI can't live solely in the IT department. Include leaders from all relevant departments from day one to ensure the strategy serves the entire business. 4. Prioritize quick wins to build momentum. Identify a few small, high-impact projects that can deliver results quickly. This builds organizational confidence and makes people ready to take on larger initiatives. 5. Invest in data foundations. The best AI strategy will fail without clean and well-governed data. A disciplined approach to data quality is non-negotiable. 6. Focus on change management. Technology is the easy part. Prepare your people for new workflows and equip them with the skills to work alongside AI effectively. 7. Create a feedback loop. An AI strategy is not a one-time plan. Continuously gather feedback from users and analyze performance data to adapt and refine your approach. The goal is to make AI a part of how you achieve your objectives, not a separate project. #AIStrategy #BusinessGoals #DigitalTransformation #Leadership #ArtificialIntelligence

The AI infrastructure conversation is changing fast. Would you agree? For years, the industry focused on one thing: More GPUs. But Agentic AI is rewriting the architecture equation entirely. In the chatbot era, one CPU could support 4–8 GPUs. Now? Production AI systems are moving toward a 1:1 CPU-to-GPU ratio — and in some deployments, even higher on the CPU side. Why? Because Agentic AI doesn’t just generate answers. It reasons, orchestrates, calls tools, manages workflows, retrieves data, coordinates services, and executes actions across complex environments. That creates an entirely new compute layer. The future AI stack will require: ⚡ GPU racks for dense AI model computation ⚡ High-performance CPUs for orchestration and inference pipelines ⚡ Massive memory bandwidth and low-latency data movement ⚡ Scalable infrastructure optimized for power efficiency and total cost of ownership This is why the role of CPUs in AI is expanding dramatically. While GPUs accelerate the models, CPUs increasingly become the control plane of enterprise AI systems. That’s also why AMD’s revised server CPU TAM projection reaching $120B by 2030 is such an important signal for the industry. AMD EPYC is becoming a foundational layer for enterprise AI infrastructure — delivering the throughput, efficiency, and scalability needed to move AI from simple responses to real-world autonomous action. The next era of AI won’t be powered by a single “AI box.” It will be powered by tightly integrated CPU + GPU infrastructure designed for intelligent systems operating at global scale. We are still in the early innings. Read more on why AMD revised the server CPU TAM to $120B by 2030: https://lnkd.in/gkJGpjsE #AI #AgenticAI #AMD #EPYC #DataCenter #ArtificialIntelligence #Infrastructure #GPU #CPU #EnterpriseAI #Innovation #Technology #FutureOfWork

6 frameworks to cut through AI noise. Leadership offsites are about choices: '𝘑𝘰𝘰𝘴𝘵, 𝘸𝘦 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘥𝘰 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘈𝘐.' '𝘎𝘳𝘦𝘢𝘵. 𝘉𝘶𝘵 𝘸𝘩𝘢𝘵 𝘸𝘪𝘭𝘭 𝘺𝘰𝘶 𝘥𝘰 𝘧𝘪𝘳𝘴𝘵? 𝘈𝘯𝘥 𝘸𝘩𝘺?' That's the moment we need frameworks - not to complicate things, but to simplify the endless options into clear decisions. The 6 frameworks that proved most effective: 1. Map your AI opportunity landscape The AI Opportunities Radar gives teams a shared language. Is this a back-office efficiency play or a game-changing customer experience? Plot it visually and watch the strategic debates become productive. 2. Balance quick wins with transformation The 'low- and high-hanging fruit' framework. Leadership teams need early momentum (quick wins) AND meaningful transformation (big bets). I usually print use cases and let them map them on these straightforward axes. 3. Where will we create value with AI "We'll be 30% more productive with AI!" Really? How? The AI Value framework forces teams to articulate exactly where and how value will emerge - beyond the vague productivity promises. It also highlights the importance of thinking beyond just productivity. 4. Start with real problems, not shiny toys The classic Value Proposition Canvas grounds everything in reality. What jobs-to-be-done can we actually do with AI, and which pains are we solving for? It's key to think from this lens instead of just getting excited about a new AI tool being launched last month... 5. Time your moves strategically The McKinsey 3 Horizons approach helps sequence your AI journey: what do we optimize now, what do we build next, and what new business models might emerge? Without this, teams might try to do everything at once and achieve nothing. 6. Build the full system, not just the tools The AI Strategy Canvas reminds us that successful AI isn't just about the technology - it's about governance, capabilities, ethics, and organizational change. The companies getting real results aren't just deploying tools; they're rewiring how they work. Leadership teams don't need another AI deck, vendor pitch or new shiny tool that will solve everything ;-) they need a map for making choices that stick. Keeping the reality of actually executing on AI in mind. Are you part of a leadership team stuck in AI paralysis? Let's grab a coffee. Creating momentum and helping you choices is what I do.