Automation Implementation Tips

If you’re an AI engineer trying to understand and build with GenAI, RAG (Retrieval-Augmented Generation) is one of the most essential components to master. It’s the backbone of any LLM system that needs fresh, accurate, and context-aware outputs. Let’s break down how RAG works, step by step, from an engineering lens, not a hype one: 🧠 How RAG Works (Under the Hood) 1. Embed your knowledge base → Start with unstructured sources - docs, PDFs, internal wikis, etc. → Convert them into semantic vector representations using embedding models (e.g., OpenAI, Cohere, or HuggingFace models) → Output: N-dimensional vectors that preserve meaning across contexts 2. Store in a vector database → Use a vector store like Pinecone, Weaviate, or FAISS → Index embeddings to enable fast similarity search (cosine, dot-product, etc.) 3. Query comes in - embed that too → The user prompt is embedded using the same embedding model → Perform a top-k nearest neighbor search to fetch the most relevant document chunks 4. Context injection → Combine retrieved chunks with the user query → Format this into a structured prompt for the generation model (e.g., Mistral, Claude, Llama) 5. Generate the final output → LLM uses both the query and retrieved context to generate a grounded, context-rich response → Minimizes hallucinations and improves factuality at inference time 📚 What changes with RAG? Without RAG: 🧠 “I don’t have data on that.” With RAG: 🤖 “Based on [retrieved source], here’s what’s currently known…” Same model, drastically improved quality. 🔍 Why this matters You need RAG when: → Your data changes daily (support tickets, news, policies) → You can’t afford hallucinations (legal, finance, compliance) → You want your LLMs to access your private knowledge base without retraining It’s the most flexible, production-grade approach to bridge static models with dynamic information. 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

🚀 95% Zero-Touch IT Offboarding with Azure Automation I recently finished building an offboarding automation that reduced IT offboarding effort by ~95%. What used to take 45–60 minutes per user now takes minutes. Here’s what Azure handles automatically 👇 ✔ Disable the user account ✔ Remove access across systems ✔ Convert the mailbox to a shared mailbox ✔ Grant the manager access to the mailbox ✔ Share the user’s OneDrive with the manager ✔ Remove licenses & reclaim costs ✔ Clean up security groups, roles, and distro lists ✔ Update the IT ticket automatically with timestamps showing: • When the account was disabled • What access was removed • Which groups and distribution lists were affected ✔ Generate audit-friendly logs & documentation The IT team only needs to: • Arrange equipment collection • Update the ticket when equipment is collected • Close it No manual checklists. No guessing when access was removed. No scrambling when an auditor asks, “When was this account disabled?” This is the real value of Azure Automation — speed, consistency, and built-in auditability. When repetitive work disappears, IT teams can focus on: • Security posture • Platform improvements • Higher-value IT projects Automation doesn’t replace people. It frees them to do better work. 💬 Still offboarding manually? There’s a better way.

A lot of teams waste millions automating the wrong processes. In the next 24 months many companies will become agentic native, so you want to do it right. That's the platform we are building at CrewAI. After helping thousands of companies build AI automations here's what actually works: The biggest mistake? Teams try to automate their most ambitious processes first. Where most of their team was not yet familiar with AI Agents. Counter-intuitive, but these are often the ones you should save a follow up candidates. Here's what actually drives results: Maximize two axis: • Consistent quality of outputs • Flexibility in the execution Focus on processes with: • High volume • Clear success metrics • Quality consistent outputs • Can benefit from more flexibility (less brittle) At CrewAI, we've developed the Intelligent Automation Framework that's now processing over 30M agent monthly. The framework works because it: • Starts small • Proves value quickly • Scales methodically • Combines human expertise with AI capabilities Our most successful clients follow this process: 1. Pick ONE high-volume, well-defined process 2. Implement proper guardrails 3. Test extensively with CrewAI's platform 4. Learn from results 5. Expand strategically The key is building workflows where AI augments humans rather than replacing them. We've seen this work across industries with partners like NVIDIA, PwC, Cloudera and IBM. But here's what makes this truly transformative: When done right, automation frees humans to focus on: • Strategic thinking • Creative problem-solving • Relationship building The future isn't about replacing humans. It's about giving them superpowers. Want to see how AI agents can transform your enterprise operations? Visit crewai.[com] to learn how we're helping Fortune 500 companies automate their most critical processes. Follow me for more insights on AI automation and the future of work. 🚀

AI is changing the economics and speed of cyberattacks. What once took threat actors days or weeks can now happen in minutes: automated reconnaissance, AI-assisted exploit development, credential targeting, lateral movement, and highly personalized phishing at scale. This is why Palo Alto Networks believes so strongly in the concept of autonomous resilience. The traditional model of security operations: fragmented tools, manual escalation paths, and human-speed response cycles - was not designed for machine-speed threats. Autonomous resilience means building security architectures that can continuously reduce exposure, validate trust, and contain threats in real time. What does that look like in practice? 🔸 Minimize attack surface Continuously identify and remediate exposed assets, misconfigurations, vulnerable APIs, and unmanaged cloud resources before attackers can weaponize them. For example, AI-driven exposure management can detect an internet-facing development environment created outside policy and trigger automated remediation immediately. 🔸 Secure every identity Trust must extend beyond employees to machine identities, workloads, APIs, and AI agents. This means enforcing least privilege, adaptive access controls, and continuous identity validation to stop credential misuse and token theft before attackers gain persistence. 🔸 Defend the software supply chain AI-assisted attacks increasingly target CI/CD pipelines, open-source dependencies, and code repositories. Organizations need runtime protections, code integrity validation, and automated policy enforcement to prevent manipulated code from reaching production environments. 🔸 Constrain blast radius Zero Trust architectures become even more critical in an AI-driven threat landscape. Microsegmentation, continuous inspection, and behavioral analytics help prevent attackers from moving laterally across environments once initial access is achieved. 🔸 Detect and respond in real time Security teams cannot rely on analysts manually correlating thousands of alerts. AI-driven SOC operations can automatically prioritize incidents, enrich telemetry, isolate compromised assets, and initiate containment workflows within minutes — dramatically reducing operational fatigue and response time. The outcome is not “fully autonomous security.” The outcome is resilient organizations that can adapt, contain, and recover faster in an increasingly automated threat environment. Cybersecurity is evolving from reactive defense into continuous operational resilience. The organizations preparing for that shift now will be far better positioned for what comes next.

I've been building and deploying RAG systems for 2+ years. And it's taught me optimizing them requires focusing on 3 core stages: 1. Pre-Retrieval 2. Retrieval 3. Post-Retrieval Let me explain - Most people focus on the generation side of things. But optimizing retrieval is what really makes the difference. Here's how to do it: 𝟭/ 𝗣𝗿𝗲-𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 This is where we optimize the data before the retrieval process even begins. The goal? Structure your data for efficient indexing and ensure the query is as precise as possible before it's embedded and sent to your vector DB. Here’s how: - 𝗦𝗹𝗶𝗱𝗶𝗻𝗴 𝘄𝗶𝗻𝗱𝗼𝘄: 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦 𝘤𝘩𝘶𝘯𝘬 𝘰𝘷𝘦𝘳𝘭𝘢𝘱 𝘵𝘰 𝘳𝘦𝘵𝘢𝘪𝘯 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘢𝘯𝘥 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭 𝘢𝘤𝘤𝘶𝘳𝘢𝘤𝘺. - 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗴𝗿𝗮𝗻𝘂𝗹𝗮𝗿𝗶𝘁𝘆: 𝘊𝘭𝘦𝘢𝘯, 𝘷𝘦𝘳𝘪𝘧𝘺, 𝘢𝘯𝘥 𝘶𝘱𝘥𝘢𝘵𝘦 𝘥𝘢𝘵𝘢 𝘧𝘰𝘳 𝘴𝘩𝘢𝘳𝘱𝘦𝘳 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭. - 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮: 𝘜𝘴𝘦 𝘵𝘢𝘨𝘴 (𝘭𝘪𝘬𝘦 𝘥𝘢𝘵𝘦𝘴 𝘰𝘳 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘐𝘋𝘴) 𝘵𝘰 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘧𝘪𝘭𝘵𝘦𝘳𝘪𝘯𝘨. - 𝗦𝗺𝗮𝗹𝗹-𝘁𝗼-𝗯𝗶𝗴 (or parent) 𝗶𝗻𝗱𝗲𝘅𝗶𝗻𝗴: 𝘜𝘴𝘦 𝘴𝘮𝘢𝘭𝘭𝘦𝘳 𝘤𝘩𝘶𝘯𝘬𝘴 𝘧𝘰𝘳 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨 𝘢𝘯𝘥 𝘭𝘢𝘳𝘨𝘦𝘳 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘴 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘧𝘪𝘯𝘢𝘭 𝘢𝘯𝘴𝘸𝘦𝘳. - 𝗤𝘂𝗲𝗿𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴 𝘭𝘪𝘬𝘦 𝘲𝘶𝘦𝘳𝘺 𝘳𝘰𝘶𝘵𝘪𝘯𝘨, 𝘲𝘶𝘦𝘳𝘺 𝘳𝘦𝘸𝘳𝘪𝘵𝘪𝘯𝘨, 𝘢𝘯𝘥 𝘏𝘺𝘋𝘌 𝘤𝘢𝘯 𝘳𝘦𝘧𝘪𝘯𝘦 𝘵𝘩𝘦 𝘳𝘦𝘴𝘶𝘭𝘵𝘴. 𝟮/ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 The magic happens here. Your goal is to improve the embedding models and leverage DB filters to retrieve the most relevant data based on semantic similarity. - Fine-tune your embedding models or use instructor models like instructor-xl for domain-specific terms. - Use hybrid search to blend vector and keyword search for more precise results. - Use GraphDBs or multi-hop techniques to capture relationships within your data. 𝟯. 𝗣𝗼𝘀𝘁-𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 At this stage, your task is to filter out noise and compress the final context before sending it to the LLM. - Use prompt compression techniques. - Filter out irrelevant chunks to avoid adding noise to the augmented prompt (e.g., using reranking) 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: RAG optimization is an iterative process. Experiment with various techniques, measure their effectiveness, compare them and refine them. Ready to step up your RAG game? Check out the link in the comments.

The answer to your outbound problems isn't: ⛔️ AI ⛔️ More volume ⛔️ SDR agents ⛔️ More relevance ⛔️ Dialers It's your OFFER. Let me explain... Most reps reach out with something like: “Just want to introduce myself and our company…” “Let’s do a quick call so you know your options when budgeting season comes around...” The problem? You have NOTHING to offer. If there’s no immediate need, there's zero reason to take a meeting with you. So you need a way to entice buyers to meet when they have a problem, but are not actively shopping. Here are three types of offers you can use to entice buyers to meet with you: ✅ Offer #1: Good - Pitch The Blind Date Position who the buyer will be meeting with. Hype up the AE, sales engineer, or yourself. Show them that meeting with you will be worth their while. Example: A client of ours sells an automated welding solution. The manufacturing industry is facing a massive shortage of welding talent. Their SDRs pitched it like this: “I’d love to introduce you to Eric. He’s worked with a dozen manufacturers like Caterpillar, Karavan, and more, who are all facing similar challenges. He’ll walk you through how they’re automating the most difficult welds and dealing with the labor shortage. Even if nothing comes of it, you’ll walk away with a better understanding of how the industry is solving this.” Even if the buyer isn’t shopping, they gain value from the conversation itself. ✅ Offer #2: Better - 1:Many Offers These are high-quality, reusable insights that still feel tailored. Think: competitive benchmarks, industry research, or best practice guides. Example: We have a client that sells to ecomm brands. They conducted a mystery shop of 400 competitors to analyze response times, customer service channels, etc. Their reps used those insights to open cold calls with: “Hey Katie, I submitted a ticket on your site, and it took about 48 hours to get a response. It was about 3x longer than folks like Patagonia and the North Face. Again, it’s Jason. Mind if I share more about why I’m calling?” That’s an offer that feels immediately relevant and valuable. It gets a conversation started immediately. ✅ Offer #3: Best - 1:1 Offers These are custom-tailored experiences or resources created specifically for the prospect. It’s you and your organization putting in serious effort to customize the offer. This works best at the enterprise & strategic levels. Examples: - A cyber risk analysis - A benchmarking analysis - A workshop - A personalized audit of a website checkout flow. - Visiting and experiencing the brand firsthand, then sharing insights. - Offering free data, licenses, or pilots. These take more work, but they convert like crazy. ~~~ Which one's most applicable for you?

Sales folks, take note! Spamming a target company's employees with your services and requests for meetings will result in your company making its way onto a buyer's blocklist. As a buyer in the localization industry, I receive dozens of emails and LinkedIn requests every single day from vendors looking to showcase translation, AI, QA services, and more. It's not humanly possible to give personal replies to every outreach. When vendors can't get through to me, they often reach out to everyone on my team... and sometimes to many others across my company. I'd love for this practice to stop. It wastes valuable company time and makes a vendor appear desperate and non-strategic. Here's what to do instead: 1. Appeal to ego! Invite a target company’s decision-maker to a panel, or start a vlog series and ask buyers to appear and discuss industry topics. It’s also a great opportunity to reposition your company as a thought leader. 2. Offer genuine insight, not just services. Share a case study, white paper, or benchmarking data that’s actually useful to the buyer’s role, and do it without a sales pitch. 3. Build a reputation before you build a pipeline. Comment thoughtfully on posts. Contribute to community conversations. If you consistently show up with value, you’re far more likely to get noticed. 4. Target smarter, not broader. Don’t shotgun your message to an entire company. Learn the org. Understand the buyer’s scope. Then send one well-researched, personalized note that shows you actually did your homework. 5. Focus on mutual value. Can you help solve a known pain point or offer perspective on something changing in the market? Frame your outreach around collaboration, not consumption. 6. Use timing to your advantage. Keep tabs on when companies are hiring for roles associated with your offerings, launching in new markets, or attending conferences. That’s when buyers are more receptive to new solutions. 7. Lead with generosity. Offer a no-strings-attached resource, intro, or suggestion that doesn’t benefit you directly. Reciprocity is a powerful trust builder. And please! Don't ever ever call me on the phone! ;)

Want to become a strong Technical Project Manager in RPA and AI? Let me share 3 things based on my experience. 1-Get your hands dirty with real bots Managing automation projects is not just about timelines and stakeholders ,it’s about understanding the process logic. If you’ve never designed or configured a bot yourself (even a small one), you’re missing a big piece of the picture. Once you build and break a few workflows in UiPath or Automation Anywhere, you start thinking differently , like an automation architect and not just a project lead. 2-Use proven delivery frameworks and templates Every RPA project follows similar stages ,discovery, design, development, UAT, deployment, and support. Yet, many teams still start from scratch every time. Having standard templates (PDD, SDD, test cases, hypercare checklist) and a delivery playbook can cut your project cycle time by 30–40%. 3-Leverage AI and analytics to manage smarter AI can now help you manage automation projects more efficiently , not just technically, but operationally. Use AI to write better documentation. Tools like ChatGPT or Copilot can help you draft PDDs, summarize process maps, or create test case outlines from your discovery notes. Analyze logs automatically. Instead of manually reviewing Orchestrator logs, use AI-powered log analyzers (like UiPath Insights, Power BI with AI visuals, or ElasticSearch dashboards) to detect recurring exceptions, long-running jobs, or unattended downtime. Automate your project tracking. Use AI to summarize daily stand-ups, extract action items, or even update Jira or Azure DevOps tasks automatically. Measure business impact continuously. Combine RPA data (execution time, volume, error rate) with business metrics (cost saved, hours returned) to build ROI dashboards that update weekly. What else you can add? Sarah Ghanem

Choosing the right embedded payments partner is not easy, and for ISVs, the stakes could not be higher.👇 The wrong decision can cost time, resources, and trust with end users. But how do you know which partner will truly deliver? Here are just some of the challenges ISVs face when evaluating providers: 🟣 Roadmap strength and transparency 👉 Without a clear and communicated product roadmap, you are left guessing whether the platform will scale with your business or remain stagnant. 🟣 Service and support 👉 It is not just about getting you live. ISVs need integration support before and after the sale, responsive account management, and a partner that feels like an extension of their own team. 🟣 Pricing clarity 👉 Many providers hide fees behind complex structures. Transparent, fair, and predictable pricing models remain the exception rather than the rule. 🟣 Training and enablement 👉 Even the best technology is wasted if your team cannot use it. Ongoing education and accessible resources make all the difference. 🟣 Geographic reach 👉 Does the provider support your current footprint and the regions where you plan to grow? A payments partner should never be the reason you cannot expand into a new market. 🟣 Product stack flexibility 👉 Too many solutions force a narrow stack. ISVs need choice, modularity, and tools that suit their users rather than the provider’s preferred model. 🟣 Sector knowledge 👉 Each vertical has its own regulatory, operational, and commercial nuances. A partner with proven experience in your market can anticipate issues before they arise. 🟣 Strategic alignment 👉 Ultimately, ISVs need a partner that shares their long term vision rather than a vendor with good technology alone. The embedded payments landscape is crowded. The best partners rise above the noise by offering real collaboration, not just capability.

RAG isn’t just about connecting a model to a vector database. It’s a complete system — with 9 moving parts that must work together to deliver reliable, context-aware responses. Over the last few months, I’ve refined this architecture while working on production-grade GenAI pipelines. Each layer has its own purpose — from ingesting and preprocessing data to evaluating and improving retrieval and generation. Here’s how it breaks down: ➟ Ingest & Preprocess: Collect, clean, and normalize data from multiple sources. ➟ Split Into Chunks: Use semantic-aware chunking to preserve meaning. ➟ Generate Embeddings: Choose embedding models based on task and domain. ➟ Store in Vector DB: Maintain a scalable vector store and metadata index. ➟ Retrieve: Combine dense, semantic, and sparse retrieval for best recall. ➟ Orchestrate the Pipeline: Use tools like LangChain or Vertex AI to automate flows. ➟ Select LLMs for Generation: Route queries to the best-fit model or gateway. ➟ Add Observability: Track performance, latency, and prompt quality. ➟ Evaluate & Curate: Continuously test retrieval and fine-tune your system. What most people miss is that RAG is iterative — not a one-time setup. Observability, evaluation, and feedback loops are what turn it from a demo into a production-ready system. If you’re building GenAI workflows, this blueprint can serve as your foundation — then adapt, optimize, and evolve it based on your data and use cases.