About
Experienced Principal Solutions Architect with 18+ years of experience in customer-facing…
Activity
60K followers
-
Prasad Rao shared thisPeople looking for a job change often face the same dilemma early on: should I prep for a specific company, or prep in a company-agnostic way? Unless you already have a call from a specific company and are in their interview process, preparing generically helps the most. I've seen both. The candidates who built a story bank first walked into interviews more prepared every time. The ones who spent three weeks on a company's culture deck knew what the interviewer wanted, yet struggled to tell stories that showed the scope and impact of their experience. This week's newsletter covers the 8 themes behind most behavioral questions and how to build a story bank around them. Read it here: https://lnkd.in/dgBGzbQB --- ➕ Follow Prasad Rao to excel in your cloud and AI career
-
Prasad Rao reposted thisPrasad Rao reposted thisJob interviews are won before the interview starts. Strong candidates do more than prepare answers. They research the company, structure their stories, practice how they communicate, and walk in knowing exactly what they want the interviewer to remember. Here are 10 strategies that can make a real difference 👇 1️⃣ Research like a pro Study the company, role, interviewer, and recent updates before the call. 2️⃣ Master the STAR method Prepare 8–10 stories around Situation, Task, Action, and Result. 3️⃣ Practice your pitch Rehearse your introduction and answers to common questions until they sound natural. 4️⃣ Prepare smart questions Bring 5–7 thoughtful questions about the role, team, priorities, and culture. 5️⃣ Show cultural fit Connect your values and working style with the company environment. 6️⃣ Quantify your impact Use numbers, percentages, revenue, time saved, or measurable outcomes. 7️⃣ Control your body language Maintain eye contact, good posture, and calm, confident communication. 8️⃣ Handle objections gracefully Prepare for gaps, career changes, failures, and other difficult questions. 9️⃣ Close strongly Show enthusiasm and ask clearly about the next steps. 🔟 Follow up strategically Send a personalized thank-you within 24 hours and reference something specific from the conversation. Also prepare for the questions that appear repeatedly: → Tell me about yourself → Why this role? → Why should we hire you? → What is your weakness? → Tell me about a challenge → Describe a failure and what you learned → What are your salary expectations? The goal is not to memorize perfect answers. It is to walk into the interview prepared with clear stories, measurable results, thoughtful questions, and confidence. ♻ Repost to help someone preparing for their next interview.
-
Prasad Rao reposted thisAI agents did not start with autonomous systems. They evolved layer by layer. A useful way to understand Agentic AI is to look at how each generation added a new capability: reasoning, retrieval, tools, memory, planning, and coordination. Here are the 6 stages 👇 1️⃣ Rule-Based Bots Simple if-then logic handles predictable questions. Fast and reliable for fixed workflows, but anything unexpected can break the flow. 2️⃣ Single-Turn LLM Chat A prompt goes in and an answer comes out. The model can generate flexible responses, but it has no persistent memory, tools, or grounding. 3️⃣ LLM + Retrieval (RAG) The model retrieves information from documents, databases, or knowledge bases before answering, helping produce more grounded responses with supporting context. 4️⃣ Multimodal LLM + Tools + Memory Now the model can work with different input types, call APIs, run code, use external tools, and remember conversation state. 5️⃣ Autonomous Agents The system moves beyond answering. It can plan a task, take actions, observe results, reflect, use memory, and retry until the goal is completed. 6️⃣ Multi-Agent Systems Multiple specialized agents work together under an orchestrator. One researches, another builds, another reviews, while shared memory and human approval help coordinate high-stakes workflows. The progression is simple: Rules → LLMs → RAG → Tools + Memory → Autonomous Agents → Multi-Agent Systems. The biggest shift is not smarter responses. It is moving from AI that answers questions to AI systems that can reason, act, collaborate, and complete work. ♻️ Repost to help others understand Agentic AI ➕ Follow Prasad Rao for more cloud and AI insights.
-
Prasad Rao shared thisAI agents did not start with autonomous systems. They evolved layer by layer. A useful way to understand Agentic AI is to look at how each generation added a new capability: reasoning, retrieval, tools, memory, planning, and coordination. Here are the 6 stages 👇 1️⃣ Rule-Based Bots Simple if-then logic handles predictable questions. Fast and reliable for fixed workflows, but anything unexpected can break the flow. 2️⃣ Single-Turn LLM Chat A prompt goes in and an answer comes out. The model can generate flexible responses, but it has no persistent memory, tools, or grounding. 3️⃣ LLM + Retrieval (RAG) The model retrieves information from documents, databases, or knowledge bases before answering, helping produce more grounded responses with supporting context. 4️⃣ Multimodal LLM + Tools + Memory Now the model can work with different input types, call APIs, run code, use external tools, and remember conversation state. 5️⃣ Autonomous Agents The system moves beyond answering. It can plan a task, take actions, observe results, reflect, use memory, and retry until the goal is completed. 6️⃣ Multi-Agent Systems Multiple specialized agents work together under an orchestrator. One researches, another builds, another reviews, while shared memory and human approval help coordinate high-stakes workflows. The progression is simple: Rules → LLMs → RAG → Tools + Memory → Autonomous Agents → Multi-Agent Systems. The biggest shift is not smarter responses. It is moving from AI that answers questions to AI systems that can reason, act, collaborate, and complete work. ♻️ Repost to help others understand Agentic AI ➕ Follow Prasad Rao for more cloud and AI insights.
-
Prasad Rao reposted thisAI agents are not improved by prompting alone. The real advantage comes from engineering everything around the model. A useful way to understand modern agent systems is through 4 layers 👇 1️⃣ Context Engineering This is the memory layer. It retrieves relevant information, filters noise, assembles the right context, manages token limits, and keeps useful outputs available for the next turn. 2️⃣ Harness Engineering This is the machine around the model. The LLM decides what to do, calls tools or sub-agents, observes results, verifies the output, and retries when something fails. 3️⃣ Loop Engineering This turns a single execution into a controlled system. The agent starts with a goal, plans the work, executes through the harness, evaluates progress, saves state, and repeats until success criteria or stop conditions are reached. 4️⃣ Graph Engineering This defines the topology of more complex agent workflows. Tasks move through agent nodes, tools, retrievers, reviewers, and human approvals while shared state and checkpoints keep the workflow coordinated. The relationship looks like this: Context → what the model knows Harness → how the model acts Loop → how the system keeps progressing Graph → how multiple steps and actors coordinate The model may generate the intelligence, but production reliability comes from the architecture wrapped around it. That is why the next generation of AI engineering is increasingly about context, control, orchestration, and feedback loops. ♻️ Repost to help others understand Agentic AI architecture ➕ Follow Prasad Rao for more cloud and AI insights.
-
Prasad Rao shared thisAI agents are not improved by prompting alone. The real advantage comes from engineering everything around the model. A useful way to understand modern agent systems is through 4 layers 👇 1️⃣ Context Engineering This is the memory layer. It retrieves relevant information, filters noise, assembles the right context, manages token limits, and keeps useful outputs available for the next turn. 2️⃣ Harness Engineering This is the machine around the model. The LLM decides what to do, calls tools or sub-agents, observes results, verifies the output, and retries when something fails. 3️⃣ Loop Engineering This turns a single execution into a controlled system. The agent starts with a goal, plans the work, executes through the harness, evaluates progress, saves state, and repeats until success criteria or stop conditions are reached. 4️⃣ Graph Engineering This defines the topology of more complex agent workflows. Tasks move through agent nodes, tools, retrievers, reviewers, and human approvals while shared state and checkpoints keep the workflow coordinated. The relationship looks like this: Context → what the model knows Harness → how the model acts Loop → how the system keeps progressing Graph → how multiple steps and actors coordinate The model may generate the intelligence, but production reliability comes from the architecture wrapped around it. That is why the next generation of AI engineering is increasingly about context, control, orchestration, and feedback loops. ♻️ Repost to help others understand Agentic AI architecture ➕ Follow Prasad Rao for more cloud and AI insights.
-
Prasad Rao reposted thisAI agents are not a single skill. They are a stack you build step by step. If you want to learn Agentic AI properly, jumping straight into multi-agent frameworks is the wrong place to start. Build the foundations first, then add autonomy. Here’s a 3-level roadmap 👇 1️⃣ GenAI & RAG Basics Start with how GenAI and LLMs work, then learn prompt engineering, model parameters, data preprocessing, RAG, vector databases, API wrappers, and tool integration. The goal: understand how models receive context, retrieve information, and interact with external systems. 2️⃣ AI Agent Essentials Next, learn what makes an AI system agentic. Explore agent frameworks, build your first agent, design workflows, add memory, evaluate outputs, introduce multi-step reasoning, multi-agent collaboration, Agentic RAG, action planning, and safety guardrails. This is where AI moves from answering to acting. 3️⃣ Advanced Agent Skills Finally, connect agents to real-world tools such as Slack, Notion, Gmail, APIs, and Python environments. Then learn autonomous loops, custom toolkits, performance optimization, and production deployment. The progression: GenAI → RAG → Tools → Agents → Memory → Planning → Multi-Agent → Guardrails → Production Do not rush to the final layer. Build each capability, test it, then combine the pieces into systems that can reliably complete real work. ♻️ Repost to help someone learning AI agents ➕ Follow Prasad Rao for more cloud and AI insights.
-
Prasad Rao shared thisAI agents are not a single skill. They are a stack you build step by step. If you want to learn Agentic AI properly, jumping straight into multi-agent frameworks is the wrong place to start. Build the foundations first, then add autonomy. Here’s a 3-level roadmap 👇 1️⃣ GenAI & RAG Basics Start with how GenAI and LLMs work, then learn prompt engineering, model parameters, data preprocessing, RAG, vector databases, API wrappers, and tool integration. The goal: understand how models receive context, retrieve information, and interact with external systems. 2️⃣ AI Agent Essentials Next, learn what makes an AI system agentic. Explore agent frameworks, build your first agent, design workflows, add memory, evaluate outputs, introduce multi-step reasoning, multi-agent collaboration, Agentic RAG, action planning, and safety guardrails. This is where AI moves from answering to acting. 3️⃣ Advanced Agent Skills Finally, connect agents to real-world tools such as Slack, Notion, Gmail, APIs, and Python environments. Then learn autonomous loops, custom toolkits, performance optimization, and production deployment. The progression: GenAI → RAG → Tools → Agents → Memory → Planning → Multi-Agent → Guardrails → Production Do not rush to the final layer. Build each capability, test it, then combine the pieces into systems that can reliably complete real work. ♻️ Repost to help someone learning AI agents ➕ Follow Prasad Rao for more cloud and AI insights.
-
Prasad Rao shared thisMy feed is packed with BeSA certificates today, and I'm loving it. Hundreds of people posting their "Agentic AI: PoC to Production on AWS" certificate after six weeks of Cohort 10. Showing up live on Saturdays, catching recordings when life got in the way and actually doing the workshops and knowledge checks every week! That consistency is the real story. Certificates are just proof. To everyone who earned theirs: well deserved. And to the volunteers who ran it all, free, every weekend: thank you. This community runs on your dedication. --- ➕ Follow @Prasad Rao to excel in your Cloud and AI career
Prasad Rao commented on a post
8h
Nikin Tharan Address changes sound minor, but they belong in the broader case-update review.
-
Prasad Rao liked thisPrasad Rao liked thisI’m officially fluent in Yapanese. The problem is that I can yap at ~220 words per minute, type at ~120 WPM, and tap on my phone at maybe 30 WPM. So I’ve been experimenting with Wispr Flow to close that gap! Instead of slowing my brain down to match my keyboard, I can just talk through what I’m thinking and turn it into usable text across the apps where I’m already working. I can also feed those flows into Claude, which process what I’ve said and suggest improvements and optimizations to my workflows. Basically: Brain goes to yap to Wispr Flow to AI agents to useful output. Maybe Yapanese is finally a marketable skill! Check out my link for 1 free month of wisprflow pro https://lnkd.in/gKdEzEFM
-
Prasad Rao liked thisPrasad Rao liked thisMicrosoft has some seriously useful AI repositories on GitHub. If you’re learning AI engineering, agents, RAG, model deployment, or GenAI systems, bookmark these: → 𝟏. 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐠𝐞𝐧𝐭 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 Build production-grade agents and multi-agent workflows. Link: https://lnkd.in/gTVSQzMi → 𝟐. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 Practical lessons for learning AI agents. Link: https://lnkd.in/gy5E3puW → 𝟑. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐟𝐨𝐫 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 21 lessons covering GenAI application development. Link: https://lnkd.in/gcuXgvut → 𝟒. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐋𝐚𝐛 Hands-on Foundry, RAG, MCP, agents, evaluation, and security. Link: https://lnkd.in/gFw4SnKW → 𝟓. 𝐎𝐍𝐍𝐗 𝐑𝐮𝐧𝐭𝐢𝐦𝐞 𝐆𝐞𝐧𝐀𝐈 Run generative models efficiently with ONNX Runtime. Link: https://lnkd.in/gvrfJjmC → 𝟔. 𝐁𝐢𝐭𝐍𝐞𝐭 Official inference framework for 1-bit LLMs. Link: https://lnkd.in/gJui5AqA → 𝟕. 𝐎𝐍𝐍𝐗 𝐑𝐮𝐧𝐭𝐢𝐦𝐞 Cross-platform ML inference and training acceleration. Link: https://lnkd.in/gaD8bBuN → 𝟖. 𝐏𝐡𝐢 𝐂𝐨𝐨𝐤𝐛𝐨𝐨𝐤 Hands-on examples for Microsoft Phi models. Link: https://lnkd.in/ggi_sNgq → 𝟗. 𝐑&𝐃-𝐀𝐠𝐞𝐧𝐭 Automate data-driven research and ML development workflows. Link: https://lnkd.in/gfTTXSGS → 𝟏𝟎. 𝐓𝐑𝐄𝐋𝐋𝐈𝐒 Generate high-quality 3D assets from text or images. Link: https://lnkd.in/gbJUNnny These aren’t repositories to simply scroll through. Clone them. Run the examples. Break things. Modify the code. Build something on top. That’s where the learning really starts. 𝗕𝗲𝗰𝗼𝗺𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗔𝗜 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟭 𝗺𝗶𝗻𝘂𝘁𝗲 𝗮 𝗱𝗮𝘆. 𝗝𝗼𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗼𝗳 𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. 👉 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗿𝗲𝗲 now → https://avsl.beehiiv.com/ Follow Aiswarya Venkitesh for more such insights!!
-
Prasad Rao liked thisPrasad Rao liked thisGreen Card interview coming up? Prepare before you walk in. An employment-based Green Card interview is not something you should approach by memorizing answers. The goal is to understand your case, organize your records, and be ready to explain what has changed since filing. Start with these 12 areas: → Know whether your case is Adjustment of Status or Consular Processing → Review your I-485, I-140, RFE responses, immigration history, and supporting records → Know your employer, job title, compensation, work location, responsibilities, and reporting structure → Prepare carefully if you changed employers after filing → Organize your interview notice, passports, I-94, civil documents, Supplement J, and employer records → Review your Form I-693 medical documentation → Compare what was true when you filed with what is true today → Revisit every Yes/No answer on your I-485 → Practice questions about your job, travel, employment history, and case changes → Prepare spouse or children records if they are derivative applicants → On interview day, answer only what is asked and never invent an answer → Be ready for approval, further review, additional evidence, or another eligibility step Your interview does not require a perfect performance. It requires a clear, truthful, organized, and consistent record. Save this checklist before your employment-based Green Card interview. Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB 🔔 Follow to stay updated on high-skilled immigration, jobs, and business #H1B #ImmigrationJourney #GreenCard #EB1A #EB5 #USImmigration
-
Prasad Rao liked thisPrasad Rao liked this30 Claude skills to upgrade your workflow. Writing code is one piece of the job. Reviewing changes, testing applications, building agents, managing infrastructure, and securing systems all need their own workflows. Here’s a collection of skills, plugins, and agents organised by engineering role—with direct links: Software Engineers → Superpowers - https://lnkd.in/gKg-7taM → Context7 - https://lnkd.in/gxR3WayT → Feature Dev - https://lnkd.in/gV8zbw-9 → Code Review - https://lnkd.in/gNW9BxA6 → Code Simplifier - https://lnkd.in/ggg8g6R3 → PR Review Toolkit - https://lnkd.in/gNBYR8r5 AI / Agent Engineers → Claude API - https://lnkd.in/gxdibmPy → Agent SDK Dev - https://lnkd.in/gQ9taNym → MCP Server Dev- https://lnkd.in/gBMVTJiP → MCP Builder - https://lnkd.in/gWQ2fzEA → Skill Creator - https://lnkd.in/gmJePZAA → Claude-Mem - https://lnkd.in/gxQkrr7T Frontend / QA Engineers → Frontend Design - https://lnkd.in/giS7Tusf → WebApp Testing - https://lnkd.in/gM6p8_YX → Playwright CLI - https://lnkd.in/gCTqKWwv → Test Master - https://lnkd.in/giNqiUxu → Playwright Expert - https://lnkd.in/gD43CKuA DevOps / SRE Engineers → Senior DevOps - https://lnkd.in/g96fAzjG → DevOps Engineer - https://lnkd.in/gjsAaVyY → Incident Commander - https://lnkd.in/g9AmZVHN → Playwright DevOps - https://lnkd.in/gzhx8-82 Data / ML Engineers → Senior Data Engineer - https://lnkd.in/gfqdwgS4 → Senior ML Engineer - https://lnkd.in/guYRfp9r → Senior Data Scientist - https://lnkd.in/ghuuPjBs → MLOps Engineer - https://lnkd.in/gnUcKZYg → Senior Prompt Engineer - https://lnkd.in/gaAFkg5j Security Engineers → Claude Security - https://lnkd.in/gvCKKNxD → Security Guidance - https://lnkd.in/gqDj48Am → Senior Security - https://lnkd.in/g3tVVH7G → Cybersecurity Skills - https://lnkd.in/gYTmubED Start with one recurring task. Choose a relevant skill, test it on your project, and refine the workflow around the results. Which would you try first? Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
Experience & Education
Licenses & Certifications
Honors & Awards
-
Outstanding Contribution to the AWS Community award
Digital Revolution Awards
Recommendations received
2 people have recommended Prasad
Join now to viewOther similar profiles
Explore more posts
Explore collaborative articles
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
Explore More