Optimizing Workflow Processes

What’s the point of a massive context window if using over 5% of it causes the model to melt down? Bigger windows are great for demos. They crumble in production. When we stuff prompts with pages of maybe-relevant text and hope for the best, we pay in three ways: 1️⃣ Quality: attention gets diluted, and the model hedges, contradicts, or hallucinates. 2️⃣ Latency & cost: every extra token slows you down, and costs rise rapidly. 3️⃣ Governance: no provenance, no trust, no way to debug and resolve issues. A better approach is a knowledge graph + GraphRAG pipeline that feeds the model the most relevant data with context instead of all the things it might need with no top-level organization. ✅ How it works at a high level: Model your world: extract entities (people, products, accounts, APIs) and typed relationships (owns, depends on, complies with) from docs, code, tickets, CRM, and wikis. GraphRAG retrieval: traverse the graph to pull a minimal subgraph with facts, paths, and citations, directly tied to the question. Compact context, rich signal: summarize those nodes and edges with provenance, then prompt. The model reasons over structure instead of slogging through sludge. Closed loop: capture new facts from interactions and update the graph so the system gets sharper over time. ✅ A 30-day path to validate it for your use cases: Week 1: define a lightweight ontology for 10–15 core entities/relations built around a high-value workflow. Week 2: build extractors (rules + LLMs) and load into a graph store. Week 3: wire GraphRAG (graph traversal → summarization → prompt). Week 4: run head-to-head tasks against your current RAG; compare accuracy, tokens, latency, and provenance coverage. Large context windows drive cool headlines and demos. Knowledge graphs + GraphRAG work in production, even for customer-facing use cases.

Just created this comprehensive Pandas cheatsheet that I wish I had when I started my  journey! After seeing fellow practitioners struggle with the same pandas operations, I decided to create a simple yet powerful reference guide: "9 Must-Know Pandas Operations for Working with Data" This is - • Focused on real-world use cases, not just syntax • Includes time-saving tips I learned the hard way • Covers both basic and advanced features • Clean, visual layout for quick reference Key sections include: - Data Import/Export tricks - Efficient data selection methods - Statistical operations - Time series handling - String manipulation - Advanced features you might not know about Perfect for: • Data Professionals (Data Engineers, Data Scientists, ML Engineer, AI Engineers, and Data Analysts) • Tech Professionals working with Data Here are a few other commands that can help you with advanced operations - 1. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗦𝗲𝗰𝘁𝗶𝗼𝗻    - 𝚙𝚍.𝚌𝚘𝚗𝚌𝚊𝚝() for combining DataFrames    - 𝚙𝚒𝚟𝚘𝚝 vs 𝚞𝚗𝚜𝚝𝚊𝚌𝚔 operations    - 𝚍𝚏.𝚛𝚎𝚗𝚊𝚖𝚎() for column renaming    - 𝚍𝚏.𝚜𝚎𝚝_𝚒𝚗𝚍𝚎𝚡() and 𝚍𝚏.𝚛𝚎𝚜𝚎𝚝_𝚒𝚗𝚍𝚎𝚡() 2. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀    - 𝚍𝚏.𝚙𝚌𝚝_𝚌𝚑𝚊𝚗𝚐𝚎() for percentage changes    - 𝚍𝚏.𝚌𝚞𝚖𝚜𝚞𝚖(), 𝚍𝚏.𝚌𝚞𝚖𝚙𝚛𝚘𝚍() for cumulative operations    - 𝚍𝚏.𝚛𝚊𝚗𝚔() for ranking values 3. 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀    - 𝚙𝚍.𝚝𝚘_𝚍𝚊𝚝𝚎𝚝𝚒𝚖𝚎() for converting to datetime    - More datetime accessors like .𝚍𝚝.𝚖𝚘𝚗𝚝𝚑, .𝚍𝚝.𝚢𝚎𝚊𝚛    - Business day operations with 𝚙𝚍.𝚘𝚏𝚏𝚜𝚎𝚝𝚜 4. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀    - 𝚙𝚍.𝚌𝚞𝚝() and 𝚙𝚍.𝚚𝚌𝚞𝚝() for binning    - 𝚙𝚍.𝚐𝚎𝚝_𝚍𝚞𝚖𝚖𝚒𝚎𝚜() for one-hot encoding    - Window functions beyond .𝚛𝚘𝚕𝚕𝚒𝚗𝚐()    - Cross-tabulation with 𝚙𝚍.𝚌𝚛𝚘𝚜𝚜𝚝𝚊𝚋() 5. 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴    - 𝚍𝚞𝚙𝚕𝚒𝚌𝚊𝚝𝚎𝚍() method    - 𝚍𝚏.𝚠𝚑𝚎𝚛𝚎() and 𝚍𝚏.𝚖𝚊𝚜𝚔()    - 𝚍𝚏.𝚌𝚕𝚒𝚙() for limiting values 6. 𝗠𝗮𝘆𝗯𝗲 𝗮 𝗡𝗲𝘄 𝗦𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗻 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴    - MultiIndex operations    - Index alignment    - Cross-section selection with .𝚡𝚜() Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

Poor chunking undermines even the best RAG systems Every irrelevant response often traces back to how you split your documents or text; get this wrong, and no amount of prompt engineering or reranking will help. So how should you chunk your content effectively? Here’s a breakdown of the main chunking approaches—and when to use each: 𝐅𝐢𝐱𝐞𝐝-𝐒𝐢𝐳𝐞 Splits text at set character limits, often with overlap • Best for: FAQ bots, uniform documents, production environments • Avoid for: Complex narratives, varied or non-uniform document structures 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 Splits intelligently: tries paragraphs, then sentences, then smaller units • Best for: Mixed content types, general-purpose retrieval • Avoid for: Documents needing strict structure preservation 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭-𝐁𝐚𝐬𝐞𝐝 Splits along natural document structure—headers, sections, tables • Best for: Structured documents like manuals, markdown, research papers • Avoid for: Unstructured text, noisy or informal content 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 Groups text by meaning using embeddings and clustering for similarity • Best for: Topic modeling, concept extraction, grouping related content • Avoid for: High-throughput pipelines due to processing overhead 𝐋𝐋𝐌-𝐁𝐚𝐬𝐞𝐝 Uses AI to identify and split by complete thoughts or propositions • Best for: Complex reasoning tasks, detailed analysis, high-value content • Avoid for: High-volume processing, cost-sensitive projects 𝐋𝐚𝐭𝐞 The game changer. Embeds the full document, then chunks with surrounding context preserved • Best for: Complex multi-section documents, cross-referential content • Avoid for: Simple Q&A tasks, budget-constrained projects 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐖𝐢𝐧𝐝𝐨𝐰 Creates chunks with overlapping boundaries to prevent context loss • Best for: Maintaining semantic continuity, robust retrieval • Avoid for: Storage-constrained or large-scale environments Choose a chunking strategy that fits your content and queries. Begin with a simple method like recursive chunking, then iterate and optimize based on your system’s performance and needs

In-house counsels didn’t go to law school to build systems. But that’s exactly what the role is evolving into. In the AI era, legal teams aren’t just reviewing contracts. They’re guiding automation, managing risk at scale, and building operational systems that touch every function from HR to finance to product. And that shift brings new demands: ➤ You can’t think in legalese anymore. You need to speak data, process, and product. ➤ You can’t just “review.” You need to build workflows that scale decision-making. ➤ You’re not just a subject-matter expert. You’re a cross-functional partner to Sales, Finance, and Procurement. In my latest article for Forbes, I break down what this transformation means for legal leaders and what companies must do to keep up. 𝗜𝘁 𝗯𝗼𝗶𝗹𝘀 𝗱𝗼𝘄𝗻 𝘁𝗼 5 𝗸𝗲𝘆 𝗶𝗱𝗲𝗮𝘀: 1/ Standardize contract templates and negotiation positions to reduce legal turnaround time. 2/ Implement legal intake systems to streamline and triage requests efficiently. 3/ Use AI tools for contract review, summarization, and data extraction to increase productivity. 4/ Track legal team performance using operational metrics like how early legal input on supplier contracts reduced dispute escalations by a certain percentage. 5/ Evaluate legal tech not on features, but on how well it integrates into daily workflows. If you’re a GC, Legal Ops leader, or CEO thinking about how legal can drive business, this one’s for you. Check out the full article from the link in the comments 👇🏼 How are you seeing the role of in-house counsels evolve in your org? #LegalTech #GC #LegalOps #AI #CLM #Forbes #InHouseCounsel #Leadership

3 Workflows I've Automated for in-house teams. ① Ask Legal ② Procurement ③ Contract Review (not just the review!) 1. Ask Legal [or any department for that matter 🤷🏼♀️] You've heard me talk about legal teams and knowledge management. Long story short, your legal team is answering the same 20 questions over and over 😵💫 A simple way to save a CHUNK of time answering questions from the business (enabling them to go faster) ALL while having complete control & keeping a human in the loop? ↪️ Set up an 'Ask Legal' bot in your comms platform. ↪️ Sync it with your knowledge base (e.g GDrive/Notion/Sharepoint). ↪️ Set up your custom instructions (Want it to tag Bob on privacy questions only, specifically on a Tuesday? No problem).  ↪️ Don't want the answer to go straight out to the business without reviewing it first? Cool, turn on co-pilot mode. The result? 60-80% fewer repetitive queries. Your team focuses on the high value things that need a human lawyer. 2. Procurement Businesses have 100's of tools, but when departments don't speak to each other you end up with duplicate tools & subscriptions 😭 💵 🚽.  What if there was a way for the business to find out in <1 minute if there was a tool available that covered their needs, before needing to spend some hard secured department budget? Moreover, what if I told you, they could kick off the internal procurement process from the comfort of your comms platform? Team member : “Do we already have a tool for X?” in Slack/Teams ✅ Bot checks knowledge base (policies, procurement tool). ✅ If a match is found, it shares the approved tool & owner to contact. ✅ If not, the bot can ask the user for more info and direct them with next steps to kick off the procurement process from inside Slack/Teams. Ensuring your users ACTUALLY follow the process, without adding friction. Did I just see your CFO cry tears of joy? 3. Third Party Vendor Contract Review & Project Management Getting AI to redline a contract (as a first pass) is a huge win, but there's still the other pieces of the process missing, like: 🤷🏼♀️ The business figuring out IF legal review is even needed (according to company policy). 📨 The business actually submitting the contract to legal. 😩 Managing review capacity within the legal team. 🖥️ Getting the legal team to log & update the PM tool. The list never ends. Legal reviews only what actually needs their eyes, turnaround times improve, and the business stops pinging the team for “update pls?” in Slack : ) TLDR; Most legal teams are drowning in admin work that could be automated. I've built all of these using simple processes and tools (that I've found most businesses have). You also know I love a good Figma flow. So I’ve built them for all three of the above (see a sneak peak below). Want the entire thing? Comment "FLOWS" and I'll send them over. Also, tell me what you want to see - more of the above or step-by-step how-to build videos?

It’s funny, but this harsh reality also highlights a serious truth: AI is powerful, but it’s not infallible. Algorithms can misinterpret context, miss nuance, or make mistakes that a human would never make. Blind trust can be dangerous, whether you’re eating a mushroom or making business decisions. So how can we question AI outputs and make better decisions? Here are a few strategies I use: Check the source – Where did the AI get its data? Is it reliable, up-to-date, and relevant to your situation? Cross-verify – Don’t take a single answer at face value. Look for supporting evidence or alternative perspectives. Consider context – AI can miss nuances that matter. Ask: “Does this recommendation make sense given my goals, constraints, and values?” Ask why, not just what – Probe AI suggestions: “Why is this solution recommended?” Understanding reasoning helps spot gaps. Add human oversight – Involve experts, mentors, or peers to validate outputs before acting. AI is a powerful partner, but decisions should still be human-led. Our judgment, skepticism, and experience are what turn insights into smart action. 💬 How do you validate AI recommendations in your work to avoid costly mistakes? #AI #CriticalThinking #Leadership #FutureOfWork #LearningAndDevelopment #TrustButVerify

At Amazon, I’ve built pipelines that move thousands of gigabytes of data. At Amazon, I’ve also built platforms used by hundreds of teams across the organization. But do you know how I got the opportunity to do these things? → It was because of one simple mindset shift: I stopped thinking like a pipeline builder. And started thinking like a product builder. Here’s what that shift looks like in real life 👇 1. Optimize for adoption, not just execution A fast Spark job is nice. But a pipeline that any team can deploy, monitor, and debug without you? That’s a game-changer. If your internal users are struggling, that’s a UX bug. 2. Design APIs, not one-off scripts Your Airflow DAGs and Glue jobs should feel like APIs. Versioned, observable, with clear inputs/outputs. That’s how you build trust at scale. 3. Surface friction like a PM If people keep pinging you for creds, schemas, or weird Athena errors, that’s a signal. Treat those moments like product bugs. Fix them once, and fix them for everyone. 4. Metrics = feedback loops In product, you track conversion. In data platforms, track usage: → How many teams use your tools? → How often do they fail? → Who’s stuck? These are your feature requests. 5. Think enablement > control Great platforms don’t block, they enable. Guardrails should guide, not restrict. Make it easy to do the right thing. I’ve learned this the hard way. When you think like a product builder, your work scales. It doesn’t stop at you. It becomes a system that helps others move faster. So next time you're building a data pipeline, ask yourself: What would this look like if it were a product? Let’s build platforms that people actually want to use.

Your talent is worthless if you can't balance it with real-world demands. Most people chase success the wrong way: - Overworking during the week - Trying to be creative on weekends - Burning out trying to do both This approach is destroying both your potential and peace of mind. Instead, here's what actually works: 1. Integration over Separation Blend creative thinking into every professional task Make every meeting a chance to innovate Turn routine work into creative experiments 2. Balance through Boundaries Set clear limits for both work and creative time Create transition rituals between different modes Respect your energy levels above all else 3. Consistency over Intensity Small creative acts daily beat big weekend projects Regular professional development trumps sporadic sprints Sustainable practices win over heroic efforts The most successful professionals I've worked with don't try to be two different people - they become one balanced individual. Ready to transform how you approach your work and life? Pick one routine task today and approach it with creative intent. ✍️ Your insights can make a difference!

LlamaIndex just unveiled a new approach involving AI agents for reliable document processing, from processing invoices to insurance claims and contract reviews. LlamaIndex’s new architecture, Agentic Document Workflows (ADW), goes beyond basic retrieval and extraction to orchestrate end-to-end document processing and decision-making. Imagine a contract review workflow: you don't just parse terms, you identify potential risks, cross-reference regulations, and recommend compliance actions. This level of coordination requires an agentic framework that maintains context, applies business rules, and interacts with multiple system components. Here’s how ADW works at a high level: (1) Document parsing and structuring – using robust tools like LlamaParse to extract relevant fields from contracts, invoices, or medical records. (2) Stateful agents – coordinating each step of the process, maintaining context across multiple documents, and applying logic to generate actionable outputs. (3) Retrieval and reference – tapping into knowledge bases via LlamaCloud to cross-check policies, regulations, or best practices in real-time. (4) Actionable recommendations – delivering insights that help professionals make informed decisions rather than just handing over raw text. ADW provides a path to building truly “intelligent” document systems that augment rather than replace human expertise. From legal contract reviews to patient case summaries, invoice processing, and insurance claims management—ADW supports human decision-making with context-rich workflows rather than one-off extractions. Ready to use notebooks https://lnkd.in/gQbHTTWC More open-source tools for AI agent developers in my recent blog post https://lnkd.in/gCySSuS3

Guide to Building an AI Agent 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 Not all LLMs are equal. Pick one that: - Excels in reasoning benchmarks - Supports chain-of-thought (CoT) prompting - Delivers consistent responses 📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning. 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰 Your agent needs a strategy: - Tool Use: Call tools when needed; otherwise, respond directly. - Basic Reflection: Generate, critique, and refine responses. - ReAct: Plan, execute, observe, and iterate. - Plan-then-Execute: Outline all steps first, then execute. 📌 Choosing the right approach improves reasoning & reliability. 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Set operational rules: - How to handle unclear queries? (Ask clarifying questions) - When to use external tools? - Formatting rules? (Markdown, JSON, etc.) - Interaction style? 📌 Clear system prompts shape agent behavior. 4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 LLMs forget past interactions. Memory strategies: - Sliding Window: Retain recent turns, discard old ones. - Summarized Memory: Condense key points for recall. - Long-Term Memory: Store user preferences for personalization. 📌 Example: A financial AI recalls risk tolerance from past chats. 5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 Extend capabilities with external tools: - Name: Clear, intuitive (e.g., "StockPriceRetriever") - Description: What does it do? - Schemas: Define input/output formats - Error Handling: How to manage failures? 📌 Example: A support AI retrieves order details via CRM API. 6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 Narrowly defined agents perform better. Clarify: - Mission: (e.g., "I analyze datasets for insights.") - Key Tasks: (Summarizing, visualizing, analyzing) - Limitations: ("I don’t offer legal advice.") 📌 Example: A financial AI focuses on finance, not general knowledge. 7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Post-process responses for structure & accuracy: - Convert AI output to structured formats (JSON, tables) - Validate correctness before user delivery - Ensure correct tool execution 📌 Example: A financial AI converts extracted data into JSON. 8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) For complex workflows: - Info Sharing: What context is passed between agents? - Error Handling: What if one agent fails? - State Management: How to pause/resume tasks? 📌 Example: 1️⃣ One agent fetches data 2️⃣ Another summarizes 3️⃣ A third generates a report Master the fundamentals, experiment, and refine and.. now go build something amazing! Happy agenting! 🤖