Optimizing Workflow Processes

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

5 Inventory Management Strategies for Streamlined F&B Operations In F&B operations, inventory management isn’t just a backend process—it’s a critical factor that impacts costs, waste, and customer satisfaction. Let’s dive into 5 proven methods tailored for F&B businesses: ⸻ 1. FIFO (First In, First Out) • What It Means: Oldest inventory is used or sold first. • Why It’s Critical in F&B: Ensures product freshness, reduces spoilage, and minimizes waste. • Example: A restaurant uses vegetables delivered on Monday before those delivered on Wednesday to maintain freshness in dishes. ⸻ 2. LIFO (Last In, First Out) • What It Means: The newest inventory is used or sold first. • When It Works: Useful for bulk storage setups or items that are easier to access when newly added. • Example: In a bar, the most recently delivered cases of beer stacked on top are used first. ⸻ 3. CIFO (Cost In, First Out) • What It Means: Inventory with the lowest cost is used or sold first. • Why It’s Useful: Optimizes profit margins by prioritizing lower-cost inventory. • Example: A QSR uses discounted cooking oil bought in bulk first before switching to a pricier batch. ⸻ 4. EFO (Expiration First Out) • What It Means: Items nearing expiration are prioritized for use or sale. • Why It’s Non-Negotiable: Essential for preventing food waste and ensuring compliance with health standards. • Example: A cloud kitchen ensures sauces with a “use by” date of May 15 are consumed before those expiring on June 1. ⸻ 5. DIFO (Damage First Out) • What It Means: Damaged or compromised goods are dealt with first. • Why It Matters: Saves storage space, minimizes losses, and salvages value where possible. • Example: A bakery discounts slightly dented cake boxes for quick sale before displaying perfect ones. ⸻ 💡 Why These Matter for F&B Operations: Efficient inventory management directly affects food costs, customer satisfaction, and sustainability. Whether you run a restaurant, QSR, or cloud kitchen, choosing the right approach ensures smooth operations and maximized profits. ⸻ 📣 What About You? • How do you manage inventory in your F&B operations? • Have you tried a combination of these methods to optimize your process? 👇 Let’s exchange ideas and help each other improve operations in the F&B space! #InventoryManagement #F&BOperations #RestaurantManagement #FoodWasteReduction #SupplyChain #EfficiencyMatters #CostOptimization #SustainabilityInF&B #OperationalExcellence #FoodIndustryInsights #QSRManagement #CloudKitchenStrategies #PerishablesManagement #FoodSafety #ProfitabilityTips

When I landed my first client, I didn’t celebrate. I panicked... “Where do I start?” “What if I mess up?” “What if they regret hiring me?” If you’re a beginner, you’ve probably felt this too. And that’s okay. After working with multiple clients, I built a simple, repeatable process that keeps things professional and stress-free. Here’s the blueprint I wish I had when I started: 𝗦𝘁𝗲𝗽 𝟭: 𝗚𝗲𝘁 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝗮𝘆𝗶𝗻𝗴 𝘆𝗲𝘀 Ask questions like: → What do they need exactly? → How often? → Who’s the target audience? → What’s their tone or brand personality? Never assume & always ask. 𝗦𝘁𝗲𝗽 𝟮: 𝗦𝗲𝘁 𝘂𝗽 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺 Even a basic Google Doc works. Include: → A quick questionnaire (goals, tone, references) → Access to past content → Brand voice notes It shows you're organized even if you’re new. 𝗦𝘁𝗲𝗽 𝟯: 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗲 → Read their past posts → Note phrases and storytelling style → Study similar creators → See what their audience responds to You’ll learn how to write as them, not just for them. 𝗦𝘁𝗲𝗽 𝟰: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝘀𝗮𝗺𝗽𝗹𝗲 𝗽𝗼𝘀𝘁 → Write 1 post from the brief → Ask for honest feedback → Edit and improve Build trust before taking on more. 𝗦𝘁𝗲𝗽 𝟱: 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘄𝗲𝗲𝗸𝗹𝘆 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 Example setup: • Monday: Share content plan • Tue–Thu: Write drafts • Friday: Deliver & get feedback • Weekend: Learn & improve Structure creates clarity. 𝗦𝘁𝗲𝗽 𝟲: 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗿𝗼 → Send regular updates → Ask when unsure → Own your mistakes Your mindset matters as much as your writing. Don’t obsess over pricing at first. Focus on delivering value and learning fast. Still got doubts? I'm just a DM away:)

Saving Lakhs Every Month - How I Implemented an AWS Cost Optimization Automation as a DevOps Engineer! When I first joined my current project as an AWS DevOps Engineer, one thing immediately caught my attention: “Our AWS bill was silently bleeding every single day.” Thousands of EC2 instances, unused EBS volumes, idle RDS instances, and most importantly — NO real-time cost monitoring! Nobody had time to manually monitor resources. Nobody had visibility on what was running unnecessarily. Result? Month after month, the bill kept inflating like a balloon. ⸻ I decided to take this as a personal challenge. Instead of another boring “cost optimization checklist,” I built a fully automated cost-saving architecture powered by real-time DevOps + AWS services. Here’s exactly what I implemented: ⸻ The Game-Changing Solution: 1. AWS Config + EventBridge: • I set up Config rules to detect non-compliant resources — like untagged EC2, open ports, idle machines. 2. Lambda Auto-Actions: • Whenever Config detected issues, EventBridge triggered a Lambda function. • This function either auto-tagged, auto-stopped idle instances, or sent immediate alerts. 3. Scheduled Cost Anomaly Detection: • Every night, a Lambda function pulled daily AWS Cost Explorer data. • If any service or account exceeded 10% threshold compared to the weekly average, it triggered Slack + Email alerts. 4. Visibility First, Action Next: • All alerts first came to Slack channels where DevOps and owners could approve actions (like terminating unused resources). 5. Terraform IaC: • Entire solution — Config, EventBridge, Lambda, IAM, SNS — all written in Terraform to ensure version control and easy replication. ⸻ The Impact: • 20% monthly AWS cost reduction within the first 2 months. • Real-time visibility for DevOps and CloudOps teams. • Zero human dependency for basic compliance enforcement. • First-time ever — proactive action before bills got out of hand! ⸻ Key Learning: “Real success in DevOps isn’t just about automation — it’s about understanding business pain points and solving them smartly.” I learned that cost optimization is NOT a “one-time” audit. It needs real-time event-driven systems — combining AWS Config, EventBridge, Lambda, Cost Explorer, and Slack. ⸻ If you’re preparing for DevOps + AWS roles today: Don’t just learn services individually. Learn how to build real-world solutions. Show how you saved time, money, and risk — that’s what companies pay for! ⸻ If you want me to share the full Terraform + Lambda GitHub repo for this cost optimization automation project, Comment below: “COST SAVER” and I will send you the link! Let’s learn. Let’s grow. Let’s solve REAL problems! #DevOps #AWS #CostOptimization #RealTimeAutomation #CloudComputing #LearningByDoing

Build the Kitchen Right, and Everything Flows. “Your kitchen doesn’t just make food, it sets the pace. When the make line breaks, so is the service.” In QSR, the problems don’t start when a customer gets frustrated. They start earlier, when your kitchen can’t keep pace. I’ve seen it at every level. Teams working flat out, FOH keeping smiles up, but the make line behind them is struggling just to stay ahead. And it’s rarely about effort. It’s about how we build the system around them. ⸻ Think of your kitchen like a relay race: Every team member has a clear lane. Every handoff is clean. No one is running backwards or crossing over. When the make line flows this way without interruption, you feel the difference across the board. ⸻ Here’s what solid kitchen operations do: ➡️ Make-line Prep stations, assembly, and packing, all in one forward direction. No crossing over. ➡️ Smart screens Live, accurate prep screens. No lost tickets. ➡️ Clear roles Prep is prep. Assembly is assembly. Packing is packing. When everyone owns their space. ➡️ Tech Good systems don’t just print tickets. They set the tone. They make the team proactive rather than reactive. ⸻ Is your kitchen helping your team move smarter or just making them work harder? — P.S. If you’re scaling and want to build kitchens that truly support your teams and your growth, let’s connect. ——— Follow #OpsWithMuhammad for practical insights on building better systems, teams, and customer journeys. #BuiltForFlow #QSR #HospitalityOperations #OperationalExcellence

Here are 5 specific actions you can take THIS WEEK to start your legal tech and/or legal innovation journey: 1. Audit your document process: Time yourself creating a standard document from scratch versus using a template. 2. Measure one key metric: What's your average response time to client emails? How long does client intake take? Pick ONE metric to track daily for the next 30 days. Data-driven decisions start with measurement. 3. Try a project management tool: Replace email chaos with Trello, Asana, Superhuman or or another task or email management tool. Create boards or new workflows for your three most active matters and invite your team. 4. Evaluate one repetitive task for automation: Identify one task you do repeatedly (client intake, NDAs, basic legal research). Research tools that could help automate it. Calculate how many hours this would save monthly. 5. Map one client process: Gather your team for 30 minutes to visually map your client intake process Identify steps that don't add client value and eliminate them. Move high-value steps earlier in the process. The choice is clear: evolve your practice or watch new legal service providers take your place. Which of these five actions will you commit to this week? The future belongs to lawyers who deliver greater value through people, process, AND technology. #legaltech #innovation #law #business #learning

𝗔𝗻𝗸𝗶𝘁𝗮: Pooja, Our new data pipeline for the customer analytics team is breaking every other day. The business is getting frustrated, and I'm losing sleep over these 3 AM alerts. 😫 𝗣𝗼𝗼𝗷𝗮: Treat pipeline like products, not ETL tools! Let me guess - you're reprocessing the same data multiple times and getting different results each time? 𝗔𝗻𝗸𝗶𝘁𝗮: Exactly! Sometimes our daily batch processes the same records twice, and our downstream reports are showing inflated numbers. How do you handle this? 𝗣𝗼𝗼𝗷𝗮: Use - 𝗜𝗱𝗲𝗺𝗽𝗼𝘁𝗲𝗻𝗰𝘆 + 𝗥𝗲𝘁𝗿𝘆 𝗟𝗼𝗴𝗶𝗰: “Make it idempotent - Use UPSERT instead of INSERT. You should be able to re-run a job 5 times and still get the same result.” 𝗔𝗻𝗸𝗶𝘁𝗮:  “So... no duplicates, no overwrites?” 𝗣𝗼𝗼𝗷𝗮:  “Exactly. And always add smart retries. API fails are temporary, chaos shouldn’t be.” Also, implement checkpointing and use unique constraints. 𝗔𝗻𝗸𝗶𝘁𝗮: That makes sense! But what about when the data structure changes? Last month, marketing added new fields to their events, and our pipeline crashed for 2 days straight! 😤 𝗣𝗼𝗼𝗷𝗮: 𝗦𝗰𝗵𝗲𝗺𝗮 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 - You need to plan for schema changes from day one. We use Avro with a schema registry now. It handles backward compatibility automatically. Trust me, this saves midnight debugging sessions! Also, consider using Parquet with schema evolution enabled. 𝗔𝗻𝗸𝗶𝘁𝗮: Sounds sensible. But our current pipeline is single-threaded and takes 8 hours to process daily data. What's your approach to scaling? 𝗣𝗼𝗼𝗷𝗮: 8 hours? Ouch! You must Design for growth.  Use 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗦𝗰𝗮𝗹𝗶𝗻𝗴, You need partition-based processing, With Spark use proper partitioning and consider Kafka partitions for streaming, or cloud-native solutions like BigQuery slots. 𝗔𝗻𝗸𝗶𝘁𝗮: But how do you catch bad data before it messes up everything downstream? Yesterday, we had a batch with 50% null values that we didn't catch until the reports were already sent to executives! 𝗣𝗼𝗼𝗷𝗮: Validate and 𝗰𝗹𝗲𝗮𝗻 𝗱𝗮𝘁𝗮 at the start!   𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗶𝗻, 𝗴𝗮𝗿𝗯𝗮𝗴𝗲 𝗼𝘂𝘁 - isn’t just a saying, it’s a nightmare! We implement multiple validation layers:   • Row count validation   • Schema drift detection   • Null value thresholds   • Business rule checks 𝘊𝘢𝘵𝘤𝘩 𝘣𝘢𝘥 𝘥𝘢𝘵𝘢 𝘣𝘦𝘧𝘰𝘳𝘦 𝘪𝘵 𝘱𝘰𝘭𝘭𝘶𝘵𝘦𝘴 𝘥𝘰𝘸𝘯𝘴𝘵𝘳𝘦𝘢𝘮 𝘴𝘺𝘴𝘵𝘦𝘮𝘴! Here's my advice from 7+ years in production: ✅ Start Simple ✅ Test Everything ✅ Security First ✅ Document Decisions 𝗔𝗻𝗸𝗶𝘁𝗮: Amazing! Thanks 𝗣𝗼𝗼𝗷𝗮 - you just saved my sanity and probably my sleep schedule! 🙏 𝗣𝗼𝗼𝗷𝗮: Anytime! Remember, great pipelines aren't built in a day! #data #engineering #bigdata #pipelines #reeltorealdata

Two weeks ago, I took on 6 new speaking contracts—keynotes, workshops, appearances. That means → Repeating admin tasks: ✔️ Terms & conditions ✔️ Media Use Agreement ✔️ Speaker bio & photo sharing ✔️ Invoicing ✔️ Travel coordination ✔️ Content Outline Deadlines ✔️ Content Submission Deadlines ✔️ Event promotion ✔️ Pre-event comms ✔️ Post-event follow-ups So last week, in one focused block of time, I built a modular AI assistant team that now will support every engagement I take on. Modular is key. Each of these assistants can be folded into other workflows, too. Designing assistants around repeatable tasks across your business is one of the smartest ways to plan. Here’s what I built: ⸻ MASTER TIMELINE ASSISTANT: When I enter the engagement type, date and details it outputs a master list of all tasks with timeline of everything that needs completed. The assistant drops that list into slack, which triggers my crew. This is mostly automated, with me bridging the gaps. ➡️ CALENDAR AND TASKS ASSISTANT: Adds items with dates and/or times (including travel) to my calendar and task lists. ➡️ REMINDERS ASSISTANT: Creates a set of reminders in ChatGPT for every task from the timeline. Each gets delivered with event context, so I never have to dig for details. This is my ‘do this and do that, by this time’ Assistant. ➡️ CONTRACT & TERMS ASSISTANT: Drafts contracts, custom terms, media use clauses, add-on pitches and speaker agreements - specific to engagement. ➡️ COMMS ASSISTANT: Pre-drafts all communications, delivered to me with recipient and send-by date: • terms/contract • content outlines • invoices (initial + final) • pre-event reminders • post-event follow-ups • review requests • Plus, a LinkedIn promo blurb ➡️ MEDIA KIT ASSISTANT: Delivers an up-to-date shirt, medium and long bios, headshot plus media images, introduction blurb, and social links. ➡️ CONTENT PLANNING ASSISTANT: Pulls meeting notes and suggests an outline, title, and short description. ⸻ It took me about 4 hours (and to be fair I’m still refining), but I’ve shaved 90% of admin time off every appearance that I do. And will ever do in the future. It took one decision: To pause. And build my team. #AI #AIConsultant

It feels great to launch a new data product, but don't forget about the work that follows afterward! Here are steps that will help to keep it relevant for a long time: 1. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗥𝗲𝘃𝗶𝗲𝘄𝘀: Business goals and data needs change over time. Establish a routine for reviewing your data product’s usage and relevance. Is it still meeting the needs of your users? 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Create channels for ongoing feedback and encourage users to report issues or suggest improvements. 3. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀: Use feedback and review outcomes to make relevant improvements. This could mean refining visualizations, adding new data points, or optimizing performance. Most data products are never truly finished. 4. 𝗘𝗱𝘂𝗰𝗮𝘁𝗲 𝗮𝗻𝗱 𝗘𝗻𝗮𝗯𝗹𝗲 𝗨𝘀𝗲𝗿𝘀: Offer training sessions for new features or changes. Enable users to fully utilize the data product, ensuring it remains a valuable tool that gets regularly used. 5. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀: Keep a changelog or documentation of updates and modifications. This transparency helps manage expectations and provides a history of the product’s progression. 6. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Continuously monitor the data product’s performance and reliability to ensure it functions well under changing conditions. Identify and address issues before they impact your stakeholders. 7. 𝗧𝗮𝗿𝗴𝗲𝘁 𝗡𝗲𝘄 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: Regularly check for opportunities to expand your data product's functionality or apply it to new business use cases. Staying proactive and anticipating needs will keep your work results relevant for a long time.     8. 𝗞𝗻𝗼𝘄 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗦𝗮𝘆 𝗚𝗼𝗼𝗱𝗯𝘆𝗲: Not all data products are meant to last forever. Recognize when a product no longer serves its purpose and plan for its retirement or replacement. This decision ensures resources are focused on tools that continue to deliver value to the business. Handling the post-launch lifecycle is an important task. Continuous improvement and alignment with changing needs will ensure your data products stay relevant for the business. What’s your experience with maintaining data products post-launch? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #dataproducts #productmanagement #careergrowth

The best pipeline cleanup is the one your team doesn’t notice. Most cleanup strategies rely on people remembering things. Update the Close Date. Add the Next Step. Revisit cold opps. And that’s why they break. We’ve shifted that load off the team using tools like Airtable and Slack: → Auto-adjust probability when an opp sits too long in-stage → Slack nudges if Next Step is outdated or missing → Auto-filter opps from forecast views after inactivity—no need to delete them This isn’t about more oversight. It’s about smarter defaults that keep things clean without constant reminders. Good systems scale. Great systems disappear into the background. -- 🔔 Follow Nathan Weill for ops strategies that support humans, not monitor them. #RevOps #PipelineManagement #Slack #Airtable #SalesOps #Forecasting #AutomationTools #GTMops