DEV Community: Prashant Lakhera

⏳ Last 12 hours to take the next step in your GenAI career.

Prashant Lakhera — Mon, 14 Sep 2026 02:45:18 +0000

Tomorrow, we officially kick off our 90-Day GenAI Interview Program for DevOps, SRE, Platform & Forward-Deployed Engineers.
We spent almost one year designing and building this program, bringing together 3 instructors with 60+ years of combined industry experience:

🔹 Prashant - GenAI for DevOps, SRE & Platform Engineering
🔹 Pramod - System Design for GenAI
🔹 Rahul - Python, DSA & problem-solving

The goal is not simply to teach you a few AI tools or help you memorize interview answers.
We want to help you understand the latest GenAI concepts companies are asking about, learn how modern AI systems are actually designed, and most importantly, build real projects that you can push to GitHub and confidently showcase to future employers.

Over the program, we will work on areas such as:
• GenAI and LLM fundamentals
• RAG and AI Agents
• LLM inference and production systems
• GenAI for DevOps/SRE workflows
• AI system design
• Python and DSA
• Real-world troubleshooting and interview scenarios
• Hands-on projects for your GitHub portfolio

The industry is changing quickly. DevOps, SRE, and Platform Engineering are not disappearing, but the engineers who understand how to combine these skills with GenAI will have a very different set of opportunities ahead of them.
You don't need to become an AI researcher.
You need to understand how AI fits into the work you already do and how to use it to become a stronger engineer.
Last 12 hours before we begin. 🚀

If you have been thinking about making the transition, this could be the right time to start.
📌 Morning Batch: https://www.ideaweaver.ai/purchase?product_id=6827463
📌 Evening Batch: https://www.ideaweaver.ai/purchase?product_id=6827464
📌 Self-Paced Batch: https://www.ideaweaver.ai/purchase?product_id=6827466
📌 Complete Program Details: https://www.ideaweaver.ai/courses/cracking-the-genai-interview-for-devops-sre-platform-forward-deployed-engineers-morning1/lectures/66591133

📌 Before You Learn AI, Strengthen Your Kubernetes Fundamentals📌

Prashant Lakhera — Sun, 13 Sep 2026 03:34:19 +0000

Before you jump into AI, strengthen your Kubernetes fundamentals.
If you are a DevOps, SRE, Platform, or Forward-Deployed Engineer (FDE), Kubernetes is still one of the most important technologies to understand well.
It is not enough to know how to run kubectl get pods.
You should understand the architecture, availability concepts, operational tooling, and access control.

Here are 5 Kubernetes topics worth revisiting:
Introduction to Kubernetes
🎥 https://www.youtube.com/watch?v=8RxzrzKnGR8
An Overview of Kubernetes Architecture
🎥 https://www.youtube.com/watch?v=EJM2WDJPEag
What Purpose Does a Pod Disruption Budget Serve in Kubernetes?
🎥 https://www.youtube.com/watch?v=U2ELt6WLjEA
Kubernetes Terminal Dashboard - k9s
🎥 https://www.youtube.com/watch?v=mUUcGBWONr4
How to Restrict User Permissions to Only List Pods in Kubernetes
🎥 https://www.youtube.com/watch?v=2lUyzrrSN-E

These topics cover some of the areas that matter most in real-world Kubernetes work:
Architecture. Availability. Operations. Troubleshooting. RBAC & Security.
AI is changing how infrastructure is built and operated, but it does not replace strong Kubernetes fundamentals.
Linux. Git. AWS. Networking. Containers. Kubernetes.
These are still the foundations on top of which AI-powered infrastructure is being built.
That is also the philosophy behind our program:
Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers
We combine GenAI, LLMs, AI Agents, Kubernetes, Cloud, Python & DSA, System Design, and interview preparation.

New batch starts September 14.
📌 Morning Batch
https://www.ideaweaver.ai/purchase?product_id=6827463
📌 Evening Batch
https://www.ideaweaver.ai/purchase?product_id=6827464
📌 Self-Paced Batch
https://www.ideaweaver.ai/purchase?product_id=6827466
📌 Complete Program Details
https://www.ideaweaver.ai/courses/cracking-the-genai-interview-for-devops-sre-platform-forward-deployed-engineers-morning1/lectures/66591133

⏳ Last 72 hours to enroll!

Prashant Lakhera — Fri, 11 Sep 2026 15:43:43 +0000

We’re kicking off our 90-Day “Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers” program on September 14.

Over the next 90 days, the focus is simple:

✔️ GenAI concepts from an engineering perspective
✔️ Building real-world AI + DevOps projects
✔️ Python, DSA & LeetCode preparation
✔️ System Design
✔️ Interview-focused scenarios and discussions
✔️ Building projects you can showcase on GitHub

The goal is not just to learn AI, it’s to understand how GenAI is changing DevOps, SRE and Platform Engineering and prepare yourself for where the industry is heading.

Only 72 hours left before we begin.

📌 Morning Batch: https://www.ideaweaver.ai/purchase?product_id=6827463
📌 Evening Batch: https://www.ideaweaver.ai/purchase?product_id=6827464
📌 Self-Paced: https://www.ideaweaver.ai/purchase?product_id=6827466

📌 Complete Program Details:
https://www.ideaweaver.ai/courses/cracking-the-genai-interview-for-devops-sre-platform-forward-deployed-engineers-morning1/lectures/66591133

📌 Website: https://ideaweaver.ai/

If you’ve been thinking about getting serious about GenAI + DevOps, this is a good time to start.

🚀 Cracking the GenAI Interview for Google: How to Troubleshoot a Slow Linux Server

Prashant Lakhera — Fri, 04 Sep 2026 00:50:25 +0000

In this session, I cover a practical Linux troubleshooting approach across:

✔️ CPU
✔️ Memory
✔️ Disk I/O
✔️ Network

The goal is to help you answer one of the most common infrastructure interview scenarios:

The Linux server is slow: how would you troubleshoot it?

🎥 Watch the video:
https://youtu.be/yvcTKmMgOIw

If you want structured preparation for GenAI + DevOps/SRE/Platform/FDE interviews, check out our 90-day program:

📌 Morning Batch: https://www.ideaweaver.ai/purchase?product_id=6827463
📌 Evening Batch: https://www.ideaweaver.ai/purchase?product_id=6827464
📌 Self-Paced Batch: https://www.ideaweaver.ai/purchase?product_id=6827466
📌 Complete Program Details: https://www.ideaweaver.ai/courses/cracking-the-genai-interview-for-devops-sre-platform-forward-deployed-engineers-morning1/lectures/66591133

📌 System Design Interviews are Changing 📌

Prashant Lakhera — Tue, 01 Sep 2026 16:11:22 +0000

One major shift I'm seeing in System Design interviews is this:
The conversation is moving beyond traditional questions like:
❌ Design a Parking Lot
❌ Design Instagram
❌ Design a URL Shortener

For many AI-focused roles, you now need to be ready to discuss systems such as:
✔️ Design an AI Chatbot
✔️ Design a RAG System
✔️ Design an LLM Inference Platform
✔️ Design a Scalable AI Application
✔️ Design AI infrastructure for production

But a lot of interview preparation still focuses heavily on traditional System Design.
We want to change that.
For our 90-day program, System Design will be taught by Pramod, an ex-Yahoo and ex-Apple engineer with 26+ years of industry experience.
The goal is not just to memorize architectures.
It is to understand how you would actually design, scale, troubleshoot, and explain modern AI systems in an interview.

🚀 Cracking the GenAI Interview for DevOps, SRE, Platform & Forward Deployed Engineers
📅 Sep 14 - Dec 12
The program covers:
✔️ GenAI & LLM Fundamentals
✔️ AI System Design
✔️ DevOps / SRE Interview Preparation
✔️ Python & DSA
✔️ LeetCode
✔️ Hands-on Labs

📌 Morning Batch: https://lnkd.in/gEGWguSE
📌 Evening Batch: https://lnkd.in/gdqXHS_g
📌 Self-Paced Batch: https://lnkd.in/gVTSSZZH

📌 Calling an LLM API Doesn’t Mean You Understand How an LLM Works

Prashant Lakhera — Sat, 29 Aug 2026 00:12:55 +0000

One common mistake I see among people entering the AI field:

They make an API call to an LLM, get a response, build a small application, and think they understand how LLMs work.

But that’s only the surface.

The real learning starts when you begin asking:

→ What is actually happening inside a neural network?

→ Why are GPUs so important for AI?

→ How does a Transformer work?

→ What are attention and tokenization?

→ How does training actually happen?

→ How do you build and train a language model yourself?

These are also the kinds of fundamentals that can separate you in a serious AI/GenAI interview. That’s exactly why I wrote:

📕 Building A Small Language Model from Scratch: A Practical Guide

It’s an 854-page practical journey that starts with the fundamentals: Neural Networks, GPUs, Transformers, tokenization, and attention, and gradually takes you toward building your own language model from scratch.

If you want to go deeper than simply calling an API, the book is still available:

📘 Gumroad: https://plakhera.gumroad.com/l/BuildingASmallLanguageModelfromScratch

📘 Leanpub: https://leanpub.com/buildingasmalllanguagemodelfromscratch

📘 Amazon: https://www.amazon.com/Building-Small-Language-Model-Scratch-ebook/dp/B0G64SQ4F8/

🚀 Looking for a structured interview-preparation path?

I’m also running a 3-month program:

Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers

The program focuses on:

→ Generative AI & LLM Engineering

→ Python & DSA

→ System Design

→ DevOps/SRE Automation with AI

→ Hands-on AI projects

→ Interview-focused preparation

📌 Morning Batch: https://www.ideaweaver.ai/purchase?product_id=6827463

📌 Evening Batch: https://www.ideaweaver.ai/purchase?product_id=6827464

📌 Self-Paced Batch: https://www.ideaweaver.ai/purchase?product_id=6827466

📌 Complete Program Details: https://www.ideaweaver.ai/courses/cracking-the-genai-interview-for-devops-sre-platform-forward-deployed-engineers-morning1/lectures/66591133

Don’t just learn how to call an LLM. Learn what is happening underneath it.

🎥 Cracking the AWS Cloud Support Engineer Interview: Recording Now Available🎥

Prashant Lakhera — Thu, 27 Aug 2026 16:03:14 +0000

Thank you, everyone, for joining yesterday’s session on Cracking the AWS Cloud Support Engineer Interview.

We discussed how to approach real-world AWS troubleshooting scenarios, structure your answers during an interview, and think like a Cloud Support Engineer rather than simply memorizing AWS services.

🎥 The recording of yesterday’s session is now available.

https://youtu.be/g8Cgg09PAEs

If you’re looking for more structured interview preparation, our 3-month program — Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers starts September 14.

The program focuses on:

✔️ GenAI for DevOps & Infrastructure Engineers
✔️ DevOps/SRE scenario-based interview questions
✔️ Python, DSA & LeetCode
✔️ System Design
✔️ AI/LLM infrastructure
✔️ Interview preparation for companies such as OpenAI, Anthropic, NVIDIA, AWS, Google, Meta, Microsoft, Apple, Tesla & SpaceX

Batches (PST):

Morning: Mon–Fri, 7–8 AM (Thursday off)
Evening: Mon–Fri, 7–8 PM (Thursday off)
Weekend: Sat–Sun, 7–9 AM

Pricing:

Live Cohort — $299 / ₹24,999
Self-Paced — $149 / ₹12,999

📌 Morning Batch: https://www.ideaweaver.ai/purchase?product_id=6827463

📌 Evening Batch: https://www.ideaweaver.ai/purchase?product_id=6827464

📌 Self-Paced Batch: https://www.ideaweaver.ai/purchase?product_id=6827466

📌 Complete Program Details: https://www.ideaweaver.ai/courses/cracking-the-genai-interview-for-devops-sre-platform-forward-deployed-engineers-morning1/lectures/66591133

📌 30 Most-Asked AWS Cloud Support Engineer Interview Questions: Free WEBINAR TONIGHT • 7 PM PST📌

Prashant Lakhera — Wed, 26 Aug 2026 17:54:06 +0000

30 Most-Asked Questions for the AWS Cloud Support Engineer Interview

If you’re preparing for an AWS Cloud Support Engineer role, I’ve put together 30 of the most commonly asked interview questions covering AWS, Linux, networking, troubleshooting, security, monitoring, and real-world production scenarios.

I’m also conducting a FREE live session to help you prepare:

✔️ Date: Aug 26

✔️ Time: 7–8 PM PST
🔗 Joining Link: https://www.linkedin.com/events/7497868527895379968?viewAsMember=true

If you’re looking for a more structured interview-preparation program, our 3-month Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers program starts Sep 14.

🔗 Morning Batch:
https://www.ideaweaver.ai/purchase?product_id=6827463

🔗 Evening Batch:
https://www.ideaweaver.ai/purchase?product_id=6827464

🔗 Self-Paced Batch:
https://www.ideaweaver.ai/purchase?product_id=6827466

🔗 Complete Program Details:
https://www.ideaweaver.ai/courses/cracking-the-genai-interview-for-devops-sre-platform-forward-deployed-engineers-morning1/lectures/66591133

📌 MCP for DevOps: GitHub and AWS MCP Server 📌

Prashant Lakhera — Wed, 26 Aug 2026 15:30:13 +0000

There are plenty of videos and tutorials explaining MCP (Model Context Protocol).

But not many explain MCP from a DevOps engineer’s point of view.

So here’s my attempt to make MCP practical for DevOps using two real-world MCP servers:

1️⃣ GitHub MCP Server: connecting AI with repositories, issues, pull requests, and developer workflows

2️⃣ AWS MCP Server: connecting AI with AWS services and cloud operations

The goal is simple: understand MCP by using it for real DevOps workflows, not just theory.

🎥 Watch here:

English: https://youtu.be/pRs8orB1Ee8?si=NC3D18kVZ-m7v4Xm

Hindi: https://youtu.be/ycadAzGFy1Y

And if you want to go deeper into GenAI, LLMs, AI Agents, MCP, RAG, Python, System Design, and AI-powered DevOps, our 3-month program starts September 14.

📌 Cracking the DevOps Interview: Start Your Preparation for Free📌

Prashant Lakhera — Sun, 23 Aug 2026 02:17:14 +0000

If you are preparing for a DevOps interview and don't know where to start, here is something that might help.
📌 I have made my book, Cracking the DevOps Interview, completely FREE.
You can use it to practice interview questions, refresh important DevOps concepts, and identify areas where you need more preparation.
🔗 Free book:
https://pratimuniyal.gumroad.com/l/cracking-the-devops-interview
But there is something important I have realized while preparing engineers for interviews:
The interview landscape is changing.
Today, companies are not only looking for engineers who understand Linux, AWS, Kubernetes, Terraform, CI/CD, and troubleshooting.
For many modern infrastructure roles, engineers are increasingly expected to understand:
✔️ Generative AI & LLM fundamentals
✔️ Python & DSA problem solving
✔️ System Design
✔️ AI Agents & automation
✔️ GPU and AI infrastructure
✔️ DevOps/SRE troubleshooting
How AI can be applied to real infrastructure problems
That is exactly why I created the 90-Day Intensive Program:
Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers
The goal is simple:
Spend 90 days preparing consistently, building the right skills, solving interview-style problems, and becoming better prepared for the next generation of infrastructure engineering roles.
📌 Live Batch Schedule - PST
Morning: Mon–Fri, 7–8 AM (Thursday off)
Evening: Mon–Fri, 7–8 PM (Thursday off)
Weekend: Sat–Sun, 7–9 AM
📌 Pricing
Live Cohort: $299 / ₹24,999
Self-Paced: $149 / ₹12,999
📌 Morning Batch:
https://www.ideaweaver.ai/purchase?product_id=6827463
📌 Evening Batch:
https://www.ideaweaver.ai/purchase?product_id=6827464
📌 Self-Paced:
https://www.ideaweaver.ai/purchase?product_id=6827466
📌 Complete Program Details:
https://www.ideaweaver.ai
If you are not ready for the program yet, start with the free book.
Start small. Learn consistently. Solve one problem at a time.
You don't need to know everything before you start. You just need to start.

📌 Understanding LLM Inference: Prefill, Decode, and KV Cache📌

Prashant Lakhera — Sat, 22 Aug 2026 17:04:11 +0000

Most people use ChatGPT every day. But have you ever wondered what actually happens after you press Enter? The answer does not magically appear all at once.

Behind the scenes, an LLM undergoes a complex inference process.

And if you are learning LLM Engineering, AI Infrastructure, GPU Infrastructure, or GenAI for DevOps, there are three concepts you should understand:

📌 Prefill — The model processes your complete input prompt.

📌 Decode — The model starts generating the response, one token at a time.

📌 KV Cache — Instead of recalculating information from all previous tokens again and again, the model remembers useful computations and reuses them.

That last part is especially important. KV Cache can make inference much faster.

But there is a trade-off: KV Cache consumes GPU memory.

And once you start serving hundreds or thousands of users, managing that GPU memory efficiently becomes a serious infrastructure problem.

This is also why inference frameworks such as vLLM and SGLang become important.

If you are coming from a DevOps, SRE, Platform Engineering, Cloud, or Forward-Deployed Engineering background and trying to move deeper into GenAI infrastructure, this is one of those foundational concepts worth understanding.

🎥 Video:
https://www.youtube.com/watch?v=05u-bgl3ETg

I’m also starting a 90-Day Intensive Program, Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers in September.

We’ll go much deeper into:

📌 Generative AI & LLM Engineering

📌 Python & DSA

📌 System Design

📌 DevOps & SRE Automation

📌 Hands-on AI Projects

📌Morning Batch: https://www.ideaweaver.ai/purchase?product_id=6827463

📌Evening Batch: https://www.ideaweaver.ai/purchase?product_id=6827464

📌Self-paced Batch: https://www.ideaweaver.ai/purchase?product_id=6827466

Image Ref: https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/

🚀 Multi-GPU Inference: explained simply 🚀

Prashant Lakhera — Fri, 21 Aug 2026 15:20:12 +0000

When people first hear multiple GPUs, it’s easy to think:

More GPUs = faster LLM.

But that’s not always the case.

The real question is:

Why do we need multiple GPUs in the first place?

There are mainly two problems:

📌 The model fits on one GPU, but we need to serve more users.

📌 The model is too large to fit on a single GPU.

And that leads us to three important techniques:

1️⃣ Data Parallelism → Copy the model

Run a complete copy of the same model on multiple GPUs and distribute incoming requests among them.

Goal: Higher throughput

2️⃣ Tensor Parallelism → Split the layer

Split computations inside a model layer across multiple GPUs.

Because the GPUs need to communicate frequently, high-speed GPU interconnects such as NVLink and NVSwitch become very important.

Goal: Run larger models and parallelize computation

3️⃣ Pipeline Parallelism → Split the layers

Different groups of model layers run on different GPUs.

For example:

GPU 0 → Layers 1–4

GPU 1 → Layers 5–8

GPU 2 → Layers 9–12

The output of one stage is passed to the next, almost like an assembly line.

Goal: Scale very large models across GPUs and nodes

The easiest way to remember all three:

Data Parallelism → Copy the model

Tensor Parallelism → Split the layer

Pipeline Parallelism → Split the layers

And for very large deployments, these techniques can also be combined using Hybrid Parallelism.

📌 Want to learn these concepts from an interview perspective?

I’m starting a 90-Day Intensive Program:

Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers

We’ll connect traditional infrastructure engineering with the skills increasingly important for modern AI infrastructure roles:

✔️ Generative AI & LLM Engineering

✔️ GPU & AI Infrastructure

✔️ Kubernetes for AI workloads

✔️ Python & DSA

✔️ System Design

✔️ DevOps & SRE

✔️ Interview-style troubleshooting scenarios

The goal is not just to learn AI terminology, but to understand how these systems actually work and how to explain and troubleshoot them during interviews.

📌 Morning Batch: https://lnkd.in/gEGWguSE

📌 Evening Batch: https://lnkd.in/gdqXHS_g

📌 Self-paced Batch: https://lnkd.in/gVTSSZZH

🎥 YouTube video

English: https://lnkd.in/gmUnipkh

Hindi: https://lnkd.in/giCH6BNP