How have software engineering fundamentals changed with agentic coding? Even when you use a coding agent to write all your code, understanding software fundamentals is important for steering your agent to make the tradeoffs you want — or to even know what tradeoffs exist to be made. Additionally, when you’re building an AI application, the AI core is often expressed through a broader software application, which you will want to help build or shape.A novice who vibe codes without understanding software fundamentals can create simple applications, but this often leads to the coding agent making bad tradeoffs in latency, availability, consistency, reliability, maintainability, simplicity, and/or cost. In such cases, the developer didn’t know such tradeoffs even existed and therefore did not steer the agent to make the right decisions for their application context.This article describes what our study of AI Engineering Skills shows are the most important things to know in software engineering. It requires being skilled at:Building full-stack applicationsManaging dataDesigning system architecturesMaking systems secure and reliableScaling and operating in productionBuilding full-stack applications. Agentic coding enables many developers who previously played more specialized roles (like front-end developer or mobile developer) to play a broader, full-stack role. A coding agent can help with parts of the development process that you might be less familiar with. However, understanding how the full stack actually works is important. Skilled developers understand the key components and concepts of front-end and back-end systems, including UI components, caching, page rendering, API choice and design, authentication, state and session management, asynchronous processing, data persistence, testing, security, and accessibility.Managing data. Data deserves special attention because it is a foundation that software is built on top of, that is relatively hard to change (even if agents help with migrations). When you know how to manage data, you can think through access patterns and use them to decide what to store and for how long. You can identify the right data models and select the appropriate storage types (such as relational tables, documents, key-value, or graphs) and infrastructure, which in turn affects speed, scalability, availability, reliability, and cost. You understand transactions, concurrency, and how to ensure your data is clean, consistent, and fresh. When needed, you can ensure proper privacy, governance, and compliance. You know how to manage the data lifecycle.As an application evolves, you also know how to evolve the data architecture with it. Deciding how to manage data requires significant human-provided context. Your AI systems will get their own input context from your data source, so if data architecture is chosen poorly, the AI doesn’t know what it doesn’t know. This is why it takes skilled intervention from someone with the relevant context and skilled at AI engineering — you! — to set it right. How to build data infrastructure for agents — rather than only traditional software or humans — is also a rapidly evolving area, and you should continue to adjust your best practices as the field evolves.Designing system architectures. When you understand the major components of the full stack of software and data, you are then better positioned to decide how to put the pieces together. Good system design requires understanding what the software is intended to do (how many users? how important is latency? how important is cost? etc.) so you can make choices about the application platform, the boundary between the frontend and backend, system decomposition, application state placement, and architectural granularity (monolith vs. microservices). You will also choose the stack (programming languages, runtimes, component/frontend/backend frameworks, data technologies) — sometimes by running experiments to evaluate options before settling on one.Further, the right architecture is a moving target, depending on the phase of the project. The simple architecture you choose to build a quick prototype may not be the right architecture to build the first production system, and that too may change as the application scales. Making these decisions requires deep technical knowledge of both software components and the application context so you can design — and evolve — the architecture to make better tradeoffs.Making systems secure and reliable. To build reliable systems, you should know how to develop testing strategies to verify the correctness of your system: What mix of unit tests and integration tests, what frameworks to use, and what level of coverage. You also know how to design around possible failures — how to handle failures (like an API hitting a rate limit), build in graceful degradation, and minimize the blast radius of failures. Additionally, rather than first writing software and then later figuring out how to secure it, the “shift left” movement is moving security work earlier in the lifecycle (to the left on a traditional project timeline). Just as all developers are moving toward becoming full stack developers, many developers are now also partly security engineers. You can now use AI tools to scan your code for vulnerabilities, check dependencies for supply chain injections, and examine your cloud configuration for attack surfaces. But doing this well still requires some knowledge of security.Scaling and operating in production. To serve real users, you will have to know how to deploy your software to production. You will benefit from knowing how to execute the software development lifecycle (SDLC) which, in addition to building and testing, includes configuring the deployment environment, deciding on release strategy, applying deployment automation (CI/CD), and understanding infrastructure as a service (IaaS).Operating in production requires putting in place observability tools, setting alerts, and managing incidents. Lastly, to scale your application, you should understand the real load and know how to scale servers, load-balance, and adapt your data infrastructure (via sharding, indexing, replication) or make architecture changes to allow your system to adapt to scale. Finally, understanding coding best practices like version control, code reviews, dependency maintenance, and how to manage technical debt helps you keep evolving your system over time.Coding agents have changed how we build software, including software that does not contain any AI components. Some parts of coding knowledge — like memorizing coding syntax — are becoming obsolete. But developers who deeply understand how software works vastly outperform those who vibe code without understanding.Understanding software fundamentals (in addition to AI) also helps you figure out what software can and cannot do. This makes them important context for how you use coding agents and shape the build. I will discuss these in future posts.

🚨 I've just published Sentence Transformers v6.0, introducing MultiVectorEncoder: ColBERT-style late interaction models are now a fourth model type, for training, inference, and interpretation, alongside the dense, sparse, and reranker models! Details:Where a regular embedding model compresses a whole text into one vector, a multi-vector model keeps one vector per token and scores query against document with the MaxSim operator. That preserves token-level matching information that a single vector has to average away. It is also the state of the art for visual document retrieval, where a text query is matched against page images directly, charts and tables included, with no OCR step in between.LightOn built PyLate on top of Sentence Transformers to close the late interaction gap, and much of what you can load today was trained with it. With v6.0, those capabilities land in Sentence Transformers itself, designed together with PyLate's authors.Any PyLate, Stanford ColBERT, or ColPali checkpoint loads straight into the same familiar API: model.encode_query(), model.encode_document(), and model.similarity() just work, whether the documents are texts or page images.Does it help? LightOn trained LateOn (multi-vector) and DenseOn (dense) on the same data with the same 149M ModernBERT backbone, and the multi-vector model wins on 9 of the 13 NanoBEIR datasets: 0.6868 vs 0.6764 mean NDCG@10. The price is a bigger index, and the new HierarchicalTokenPooling module halves it at roughly no retrieval cost.The release also moves to transformers v5, speeds up multi-column training losses by about 1.25x with a merged forward pass, and fixes a class of silent half precision scoring bugs. One of those matters a lot: a bfloat16 reranker with the default sigmoid activation collapsed its top candidates onto a handful of tied scores, which randomized their order. Upcasting before the activation took NanoBEIR NDCG@10 from 0.18 to 0.68.Antoine Chaffin and Raphael Sourty from LightOn, and I wrote a blog post walking through multi-vector models in practice: loading the various checkpoint formats, encoding and scoring, plugging them into a search stack, running them on page images, and keeping the index affordable. Check it out if you want to get started, or just point your Agent to the URL: https://lnkd.in/enBJiiXrpip install sentence-transformers==6.0.0When I published v5.4, I wrote that it set up the groundwork for introducing late interaction models in the next major release. This is that release, and it is one of the largest updates in the project's history.

What's the difference between ColBERT and ColPali?ColBERT and ColPali are both new retrieval models based on the late interaction mechanism, but how do they differ?ColBERT: Contextualized Late interaction over BERTColPali: Contextualized late interaction over PaliGemmaColBERT: single modality (text-only)ColPali: multimodal (text and images) using VLMsColBERT: documents as text onlyColPali: documents as images (screenshots of PDFs)ColBERT: https://lnkd.in/gaV3mRevColPali: https://lnkd.in/gTf68AgY

The best software I use every day is software I never think about.That is the highest compliment I can pay a product, and Wispr Flow has earned it twice.First with dictation. Somewhere last year I stopped typing and started talking. Specs, updates, hiring notes, half my thinking. 138,802 words this year, almost none of them typed. The keyboard became the exception without me noticing it happen.Then Notetaker arrived and fixed something I had quietly given up on.I used to sit in meetings scribbling, half listening, missing the actual decision because I was still writing down the sentence before it. I would walk out with three bullets and a fuzzy memory of what we agreed.Now I just listen. Properly listen. The notes are waiting when I am done, and my follow-ups are written before I have left the room. What used to take an hour of trying to remember now takes four minutes. It gave me back the ability to be present in my own meetings.Tanay Kothari & Sahaj Garg you have built something I actually enjoy using every day. It does not ask me to change how I work. It just removes what was in the way.That is what I want Creedom to be for creators. Not another tool they have to remember to open. Something that sits inside the work and quietly removes the hard part.The best products do not compete for your attention. They give it back.Nimisha Mehta Vasundhara Bhagat, excited to see where this goes in India.

A guy shared a Claude Code prompt that's literally $300/hr senior engineerHere's the full prompt to copy: P.s. get more Claude resources for absolutely FREE, sign up here: https://lnkd.in/dMGZuZAjPrompt: "Before implementingWork like a contractor who bills for rework: the cost of a wrong assumption is yours to avoid, and the cost of an unnecessary question is mine to pay.1. Investigate before you askRead the relevant code, tests, configs, and dependency manifests first. Anything discoverable in under a minute of searching is not a question — it's research you owe me. Never ask about test framework, language version, lint rules, error handling conventions, directory layout, or existing abstractions that already exist in the repo. If the codebase contradicts itself, that's worth raising.2. Then produce this, and stopGoal. One paragraph restating what I asked for in your own words, including the acceptance criteria you'll hold yourself to. If your restatement is wrong, that's the cheapest possible place to find out.Blocking questions (0–3). Only ask when a wrong answer means throwing work away, not adjusting it. Each question gets your recommended default so I can reply "yes to all" — never ask an open question where a proposed answer would do. If nothing is genuinely blocking, say so and list zero.Assumptions. Numbered, specific, falsifiable. "Inputs are under 10k rows and fit in memory" is an assumption. "The code should be maintainable" is not. Cover whichever of these the task actually touches: - Data: shape, volume, trust level, encoding, what a malformed input looks like - Failure: what should happen on timeout, partial write, or downstream 500 — retry, fail loud, or degrade - Boundaries: who calls this, what's public API vs. internal, backwards-compat obligations - State: concurrency, idempotency, transactionality, ordering guarantees - Environment: runtime version, where it deploys, what it's allowed to reach - Scope: what you're deliberately *not* doing, and what you're leaving as TODO - Testing: what you'll write tests for and what you'll leave uncoveredPlan. Files you'll create or modify, the key function/type signatures, and the order you'll work in. Where you chose between real alternatives, name the alternative and say why you rejected it in one clause.Then wait. Do not begin implementing.3. ProportionalityThis ceremony scales with blast radius. A typo fix, a rename, or a change under ~20 lines with one obvious correct form: just do it. A new module, a schema change, anything touching auth, money, migrations, or deletion: full treatment, and be more suspicious than usual of your own assumptions."Credit: Minchoi

🚨 The #1 problem with local AI is now solved.There’s a new free tool called CanIRun AI that checks your hardware and tells you which models will actually run well before you download anything.So instead of guessing and hitting out-of-memory errors…it grades every model against your machine.What it does (right in your browser, no install):→ detects your setup (RAM / CPU / GPU / VRAM)→ scores each model for fit, speed, and context length→ grades every quantization level (Q4_K_M, Q6_K, Q8_0, etc.)→ labels what runs great vs okay vs too heavyIt covers most of the open-weight stack, Llama, Qwen, Gemma, Mistral, DeepSeek, Phi and more, pulling requirements from llama.cpp, Ollama, and LM Studio.Link: canirun.ai Screenshot 2026-08-24 at 6.17.11 PM100% opensource

You can now run a 2.8 TRILLION parameter model on a 4GB GPU.It’s called AirLLM, an open-source tool that uses "Layer-wise Inference." It only loads one layer onto your GPU at a time. so the VRAM you need depends on the layer size, not the model size.No quantization. No distillation. No pruning.→ DeepSeek-V3 (671B) on 12GB→ Llama 3.1 405B on 8GB→ Kimi K3 (2.8 TRILLION params) on under 4GB→ works with almost every open modelThe biggest model on it needs the LEAST VRAM. K3 is sparse MoE, so it streams only the experts a token actually routes to instead of a whole dense layer.2.8 trillion parameters running in less VRAM than a 70B.

Hey everyone!Just brushed up on my SQL fundamentals again while sitting through 8-hour-LONG!!!! tutorials. 😭I’ve noticed that even some paid courses don’t cover very basic concepts despite having 6+ hours of content.So, I decided to create a single ".ipynb" file that covers SQL from fundamentals to advanced concepts, with hands-on examples throughout.Here’s the link: https://lnkd.in/gsHP_wx9Let me know what you think And if you ever have doubts while learning, feel free to irritate me with DMs. 😂Hope it helps someone!

The University School of Computing, Rayat Bahra University, Mohali successfully organised the IEEE International Conference on Secure Information Systems and Technologies (ICSIST 2026) from 17–19 August 2026 at Rayat Bahra University.The conference provided a vibrant international platform for researchers, academicians, industry professionals, scientists, and practitioners to exchange ideas and present cutting-edge research in cybersecurity, secure information systems, artificial intelligence, blockchain, IoT, cloud and edge computing, digital forensics, privacy, and emerging technologies.The event featured keynote and invited talks, technical paper presentations, expert sessions, knowledge exchange, and opportunities for international research collaboration, bringing together distinguished experts from academia and industry across the globe.I would like to thank our program chair, organising chair, keynote speakers, session chairs, technical programme committee, speakers, reviewers, authors, delegates, volunteers, and the entire organising team for their valuable contributions in making ICSIST 2026 a successful academic event.🌐 IEEE ICSIST 2026📅 17–19 August 2026📍 Rayat Bahra University, Mohali, Punjab, IndiaCongratulations to the entire University School of Computing, Rayat Bahra University team! 👏Dr. Sahil Verma, B.T, M.T, Ph.D.(C.S.E) Postdoc-3, SMIEEE,ACM,IAENG#ICSIST2026 #IEEE #RayatBahraUniversity #UniversitySchoolOfComputing #CyberSecurity #SecureInformationSystems #ArtificialIntelligence #EmergingTechnologies #Research #Innovation #InternationalConference #AcademiaIndustryCollaboration #IEEEConference #RBU

OpenWorker -- an open source agent that doesn't just chat but completes tasks on your laptop -- just released a new version with many features for security workflows. After our initial release, many users found it especially useful for cybersecurity. Attackers are already using AI; OpenWorker is committed to giving defenders the same leverage. Running an agent requires both (i) A model and (ii) A harness (the software around the model). Because the OpenWorker harness is fully open source, security teams can audit it to make sure we haven't built any backdoors that exfiltrate your code and data to some company or even a foreign adversary.OpenWorker now comes with built-in cybersecurity agents for (i) Scanning your code for vulnerabilities. (ii) Scanning dependencies for supply chain injections. (iii) Checking your cloud security configuration for attack surfaces. This enables developers to do much more security work before deployment (part of what's called the "shift left" movement).You choose the model: you can run open weight models fully locally so sensitive code never leaves your machine. This helps with legitimate security work (like reproducing a known exploit to defend against it) that can trigger refusals in leading closed models. Or use your ChatGPT subscription, or stealth preview models like Ox Alpha, or any model via API key.Thanks also to all the open source contributors!Join work with Rohit Prsad so please follow him too to get more frequent updates. Try it out: https://openworker.com/Code: https://lnkd.in/gvZYspRv

Asked in Senior GenAI Interview Interviewer: Your LLM supports a 128K-token context window, but answer quality drops significantly when prompts exceed 50K tokens.Question:Why can performance degrade despite the model supporting 128K tokens? How would you diagnose and improve long-context performance?Explanation:1.Context window ≠ quality guarantee: 128K means the model can accept that many tokens, not that it can use every token equally well.2. Lost-in-the-middle: Important information placed in the middle of a very long context may receive less attention.3.Noise: Too many irrelevant documents can dilute the useful evidence and confuse the model.4. Attention cost: Longer sequences increase attention computation and can increase latency/memory pressure.5.Diagnose: Test different context sizes, position of relevant information, retrieval Recall@K, answer accuracy, and token usage.6.Improve: Use better retrieval/reranking, remove redundant chunks, summarize older context, prioritize relevant information, and use hierarchical/targeted retrieval instead of sending the entire 128K context.______________________________________Preparing for an ML or AI interview or Looking for Transition in AI?Check out the resource below, it covers key concepts with 1000 interview question Answers, Roadmap, Projects and practical topics to help you prepare with confidence.

Holy 😳 : OpenAI says its first custom inference chip is already beating Nvidia GB200 and GB300 systems on speed and efficiency.Across GPT‑OSS 120B, DeepSeek R1 670B, and Kimi K2.5 1T, Jalapeño delivered 1.5–1.9× more AI work per watt at peak throughput and 1.7–3.6× lower end-to-end latency in OpenAI’s own InferenceX testing!For highly interactive workloads, OpenAI reports 2.1–4.1× higher performance.The chip is rated at 700 watts, but remained at or below 550 watts during the tested workloads.OpenAI plans to begin deploying Jalapeño by the end of 2026. Gen 2 is already deep in development, with Gen 3 taking shape.Probably thats why Tibo said that in 1-2 years 750token/s will be the defaultREPOSTED

London, New York, LA and San Francisco offer the WORST value on earth 💸Valerii Emelianov mapped 100 cities on cost of living vs quality of life using Numbeo data:▫️ Migration magnets rank worst: high costs, expensive housing, services that stop matching expectations▫️ Porto, Valencia and Prague have held the top of the value ranking for 7 years, and all three keep drifting right as costs climb▫️ Europe has the widest spread on the planet, from Switzerland to the Balkans▫️ Sun Belt and Pacific Coast cities are climbing fast on domestic migration▫️ Some cheap winners are pockets of comfort for elites and tourists rather than the median residentQuality of life here means purchasing power, safety, healthcare, housing, traffic, pollution and climate

As a mom to a 3-year-old, I recently started researching schools in Charlotte — and quickly found myself jumping between school websites, maps, academic data, and lots of browser tabs.That sparked a question: 𝗖𝗼𝘂𝗹𝗱 𝗜 𝘂𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘀𝗶𝗺𝗽𝗹𝗲𝗿 for parents?So I built 𝗖𝗵𝗮𝗿𝗹𝗼𝘁𝘁𝗲 𝗦𝗰𝗵𝗼𝗼𝗹 𝗦𝗲𝗮𝗿𝗰𝗵. 🎓What started as a simple school finder evolved into three parts:📍 𝗙𝗶𝗻𝗱 𝗦𝗰𝗵𝗼𝗼𝗹𝘀 — discover nearby schools on an interactive map and filter by level and type.📊 𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝗦𝗰𝗵𝗼𝗼𝗹𝘀 — compare schools using official NC performance, growth, math, and reading data.🤖 𝗔𝗜 𝗦𝗰𝗵𝗼𝗼𝗹 𝗔𝗱𝘃𝗶𝘀𝗼𝗿 — ask questions in plain English and get answers grounded in the same school data.For now, the app is intentionally restricted to Charlotte-Mecklenburg Schools (CMS) and relies on official CMS and NC DPI data wherever possible.What makes this project special to me is that it started with a real question in my own life — how do I make a thoughtful school decision for my daughter? — and became an opportunity to explore how AI can make real-world information easier to navigate.The goal was to build a practical application that helps parents explore Charlotte-area schools through a streamlined interface instead of manually piecing information together.🔗 Try it here:https://lnkd.in/eFz-23e3I’d love feedback, especially from Charlotte parents: What else would you want to know when researching a school for your child?A big shout out to Aishwarya Srinivasan, Arvind Narayanamurthy and The Gen Academy for demystifying so many of the buzzwords around AI.#AI #AgenticAI #GenAI #EdTech #Python #Streamlit #CharlotteNC #BuildInPublic

Open-source framework for building real-time voice AI agents!Pipecat is a Python framework for orchestrating audio, video, AI services, transports, and conversation pipelines. Voice-first architecture with pluggable components.What you can build: voice assistants, AI companions, multimodal interfaces, interactive storytelling, business agents (customer support, intake), and complex dialog systems.The framework handles speech recognition, text-to-speech, conversation logic, and real-time interaction. WebRTC and WebSocket transport built in. Ultra-low latency for natural conversations.Why Pipecat:• Voice-first: Integrates STT, TTS, and conversation handling in one framework • Pluggable: Supports multiple AI service providers for each capability • Composable pipelines: Build complex behavior from modular components • Real-time: Low-latency interaction with streaming audio/videoSupported services:• Speech-to-Text: Deepgram, AssemblyAI, OpenAI Whisper, Groq, Azure, AWS, Google, and more • LLMs: OpenAI, Anthropic, Gemini, Groq, Mistral, Ollama, AWS, Azure, and more • Text-to-Speech: OpenAI, ElevenLabs, Deepgram, Cartesia, Azure, AWS, Google, and more• Speech-to-Speech: OpenAI Realtime, Gemini Multimodal Live, AWS Nova Sonic, Ultravox, Grok Voice AgentI've shared link to the repo in the comments!

neo4j-labs/agent-memory gives agents three memory layers in one graph: conversation history, a long-term entity graph on the POLE+O model, and reasoning traces of what tools it called. Ships an MCP server and LangChain adapters. The audit trail links a reasoning step to the entities it touched, but only for tool calls mapped by hand; an unmapped call disappears from that query. 435 stars, Apache-2.0.#KnowledgeGraphs #Neo4j #AgentMemory #GraphDatabase

Your private AI agent shouldn’t require Docker and a terminal.Skales is a local-first desktop AI agent for people who want an agent to work with their files, browser, calendar, email, and code.It helps you delegate multi-step work from your own computer by combining background goals with desktop tools, coding workflows, and a choice of hosted or local models.Key features:• One-click desktop setup – install it on Windows, macOS, or Linux without Docker or terminal setup• Background goals – hand it a multi-step goal, close the chat, and let it continue until completion or a decision is needed• Built-in code workflows – bind a folder, review inline diffs, run tests, and undo file changes• Flexible model access – start with the built-in trial, connect 15+ providers, or run offline with Ollama or LM Studio• Mobile access – pair Android or iOS with your desktop via QR to use its tools remotelyPublic GitHub repo; the current Skales app is closed source under BSL 1.1.🔗 GitHub: https://lnkd.in/dYtj5U99⸻♻️ Share this with your network if you found it useful or insightful.✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): dankornas.substack.com

I wasted 3 months building agents with LangChain before realizing LangGraph exists😭😭Here’s why I switched.LangChain works. You ship agents, it’s fine. But managing state between agent calls? You’re writing custom logic. Agent runs, you parse output, decide what’s next, route manually. Flexible but messy. One bad parse and your whole flow breaks.LangGraph is different. It’s designed specifically for agent orchestration.Instead of imperative loops, you define nodes (agent steps) and edges (transitions). State flows automatically through the graph. Agent A outputs to Agent B. Agent B loops back to A if needed. Routing logic? Built-in. State management? Built-in. Retry logic? Built-in.Practical example: I had an agent that needed to re-evaluate its own output. In LangChain, I manually tracked iteration count and state. In LangGraph, I defined a cycle in the graph. Clean.Another advantage: visualization. You see your agent flow as an actual graph. “Why didn’t Agent B get called?” Look at the graph, find the bug in 30 seconds instead of 30 minutes in logs.Quick decision tree:• Simple chains or chatbots? LangChain is fine. • Multiple agents, complex routing, state management? LangGraph wins. Less boilerplate, cleaner abstractions.For agentic AI, LangGraph is the better choice.#GenAI #AgenticAI #LangChain #LangGraph #LLMEngineering #SystemDesign #Python

Recently, I faced an interview for a hashtag#Generative hashtag#AI Engineer role, and the interview was heavily focused on RAG, LLMs, and production-grade AI systems.I thought of sharing some of the important RAG questions that were discussed during the interview. Hopefully, these will be helpful for anyone preparing for GenAI / AI Engineer / LLM Engineer roles. 🚀🔥 hashtag#Important hashtag#RAG Interview Questions🔹 What is RAG and why do we need it?🔹 RAG vs Fine-tuning — when would you choose which?🔹 Explain the complete RAG architecture.🔹 How do you decide chunk size and chunk overlap?🔹 What are embeddings and how do they work?🔹 How does vector similarity search work?🔹 How do you choose Top-K?🔹 What is Hybrid Search?🔹 What is Hybrid RAG and how is it different from Hybrid Search?🔹 Why do we need reranking?🔹 What is Query Rewriting and why is it useful?🔹 How do you improve poor retrieval quality?🔹 How do you reduce hallucinations in RAG?🔹 How do you handle questions when relevant information is not available in the knowledge base?🔹 How do you evaluate Retriever performance?🔹 How do you evaluate the final LLM response?🔹 How do you identify whether an issue is with Retrieval or Generation?🔹 How would you handle a large context window?🔹 How do you reduce latency in a production RAG system?🔹 How would you scale RAG from 100 queries to 100K+ queries?🔹 How would you scale a vector database from 10K to 1M+ documents?🔹 Where would you use caching in RAG?🔹 How do you monitor a RAG system in production?🔹 What are common security risks in RAG?🔹 What is Prompt Injection and how do you prevent it?🔹 How do you implement Guardrails in a RAG application?🔹 How would you design a multi-tenant RAG system?💡 Key takeawayModern GenAI interviews are going beyond just asking “What is RAG?”Interviewers are increasingly focusing on how you would build, evaluate, optimize, secure, monitor, and scale RAG systems in production.If you're preparing for a GenAI / LLM / RAG Engineer interview, I hope this list helps you identify the areas you should focus on.I’ll be covering these questions with practical explanations and real-world architecture examples in upcoming posts.What RAG topic do you find the most challenging?

Microsoft just open-sourced one of the best AI data analysis tools out there.It’s called data-formulator. Connect any source, CSV, Postgres, Bigquery, even a live URL, then build charts with a mix of drag-and-drop and plain english. The AI agent writes the SQL and transforms underneath and hands you a chart you can actually edit, not a code dump.- drag fields onto x/y/color and it builds the chart- anchor a cleaned result so follow-ups don't drift back to raw data- branch any chart to explore a variation - connect live data with auto-refresh