Avoiding Busywork With LLM Tools

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

I’ve been building and managing data systems at Amazon for the last 8 years. Now that AI is everywhere, the way we work as data engineers is changing fast. Here are 5 real ways I (and many in the industry) use LLMs to work smarter every day as a Senior Data Engineer: 1. Code Review and Refactoring LLMs help break down complex pull requests into simple summaries, making it easier to review changes across big codebases. They can also identify anti-patterns in PySpark, SQL, and Airflow code, helping you catch bugs or risky logic before it lands in prod. If you’re refactoring old code, LLMs can point out where your abstractions are weak or naming is inconsistent, so your codebase stays cleaner as it grows. 2. Debugging Data Pipelines When Spark jobs fail or SQL breaks in production, LLMs help translate ugly error logs into plain English. They can suggest troubleshooting steps or highlight what part of the pipeline to inspect next, helping you zero in on root causes faster. If you’re stuck on a recurring error, LLMs can propose code-level changes or optimizations you might have missed. 3. Documentation and Knowledge Sharing Turning notebooks, scripts, or undocumented DAGs into clear internal docs is much easier with LLMs. They can help structure your explanations, highlight the “why” behind key design choices, and make onboarding or handover notes quick to produce. Keeping platform wikis and technical documentation up to date becomes much less of a chore. 4. Data Modeling and Architecture Decisions When you’re designing schemas, deciding on partitioning, or picking between technologies (like Delta, Iceberg, or Hudi), LLMs can offer quick pros/cons, highlight trade-offs, and provide code samples. If you need to visualize a pipeline or architecture, LLMs can help you draft Mermaid or PlantUML diagrams for clearer communication with stakeholders. 5. Cross-Team Communication When collaborating with PMs, analytics, or infra teams, LLMs help you draft clear, focused updates, whether it’s a Slack message, an email, or a JIRA comment. They’re useful for summarizing complex issues, outlining next steps, or translating technical decisions into language that business partners understand. LLMs won’t replace data engineers, but they’re rapidly raising the bar for what you can deliver each week. Start by picking one recurring pain point in your workflow, then see how an LLM can speed it up. This is the new table stakes for staying sharp as a data engineer.

LLMs are the single fastest way to make yourself indispensable and give your team a 30‑percent productivity lift. Here is the playbook. Build a personal use‑case portfolio Write down every recurring task you handle for clients or leaders: competitive intelligence searches, slide creation, meeting notes, spreadsheet error checks, first‑draft emails. Rank each task by time cost and by the impact of getting it right. Start automating the items that score high on both. Use a five‑part prompt template Role, goal, context, constraints, output format. Example: “You are a procurement analyst. Goal: draft a one‑page cost‑takeout plan. Context: we spend 2.7 million dollars on cloud services across three vendors. Constraint: plain language, one paragraph max. Output: executive‑ready paragraph followed by a five‑row table.” Break big work into a chain of steps Ask first for an outline, then for section drafts, then for a fact‑check. Steering at each checkpoint slashes hallucinations and keeps the job on‑track. Blend the model with your existing tools Paste the draft into Excel and let the model write formulas, then pivot. Drop a JSON answer straight into Power BI. Send the polished paragraph into PowerPoint. The goal is a finished asset, not just a wall of text. Feed the model your secret sauce Provide redacted samples of winning proposals, your slide master, and your company style guide. The model starts producing work that matches your tone and formatting in minutes. Measure the gain and tell the story Track minutes saved per task, revision cycles avoided, and client feedback. Show your manager that a former one‑hour job now takes fifteen minutes and needs one rewrite instead of three. Data beats anecdotes. Teach the team Run a ten‑minute demo in your weekly stand‑up. Share your best prompts in a Teams channel. Encourage colleagues to post successes and blockers. When the whole team levels up, you become known as the catalyst, not the cost‑cutting target. If every person on your team gained back one full day each week, what breakthrough innovation would you finally have the bandwidth to launch? What cost savings could you achieve? What additional market share could you gain?

Perplexity recently put out an "AI at Work" guide. It's a practical read, and it's packed with patterns you can reuse even if your team runs on ChatGPT, Claude, Gemini, or something else entirely. Here are 5 takeaways I'm adopting (with copy/paste examples): 1) Fix the workflow friction before you chase "smart outputs." Most productivity loss isn't about model quality—it's context switching. Example: After a 45-minute meeting, paste your rough notes and ask: "Summarize decisions, open questions, and owners. Output: a Slack update + a Jira-ready task list." Now you're not re-listening, rewriting, and copying into 3 different tools. 2) Prompt for outcomes + format, not keywords. LLMs respond better when you specify the artifact you actually need. Instead of "Help with product launch," try: "Create a 1-page launch plan for Feature X. Audience: GTM + Eng. Include timeline, owners, risks, and launch checklist. Max 350 words." 3) Delegate like you would to a strong teammate: steps + constraints + definition of done. Single-shot prompts are fine. Multi-step prompts are dependable. Example: "Step 1: Identify 3 plausible root causes from this incident summary. Step 2: Ask me 5 clarifying questions. Step 3: Draft a postmortem with: impact, timeline, root cause, fixes, follow-ups." 4) Standardize quality with reusable "prompt assets." The unlock here is repeatability—not clever prompting. Example: Create a "Weekly Exec Update" template: "Write in 6 bullets: outcomes, metrics, risks, asks, next week priorities, dependencies. Keep each bullet < 18 words." Reuse it every Friday. Your updates become consistent across the team. 5) Close the loop with judgment + lightweight verification. LLMs accelerate work, but you still own correctness. After it drafts a customer email or a PRD, ask: "List assumptions you made. What could be wrong? What would you verify? Provide 3 counterarguments." This catches hallucinations and sharpens decision quality. Perplexity wrote the guide for their product, but these patterns feel tool-agnostic—they're really about building better workflows See comments for link to full report

Just had a major realization that's changing how I work with AI tools. We've all heard "𝐦𝐨𝐫𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 = 𝐛𝐞𝐭𝐭𝐞𝐫 𝐚𝐧𝐬𝐰𝐞𝐫𝐬" but I'm finding the opposite can be true! I've seen it firsthand - feed an LLM too much information without proper management and you get what I call "Context Distraction." The AI becomes overwhelmed, fixates on irrelevant details, and starts repeating itself instead of generating fresh insights. It's like trying to have a productive conversation with someone who's reading through a 200-page transcript of everything you've ever discussed. At some point, focus gets lost. Two approaches that have dramatically improved my results: 1️⃣ 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐒𝐮𝐦𝐦𝐚𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Instead of dumping entire conversation histories into new prompts, I summarize key points and decisions. This gives the AI a clean slate with just the essential context. 2️⃣ 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐎𝐟𝐟𝐥𝐨𝐚𝐝𝐢𝐧𝐠: Breaking complex projects into discrete conversations rather than one massive thread. I keep track externally (basic notes work fine) and only introduce relevant information when needed. The difference in output quality is remarkable. My conversations are more focused, responses are more creative, and I'm getting better solutions faster. Who else has noticed this pattern? Any other techniques you've found effective for managing AI context? #ArtificialIntelligence #LLMs #ProductivityHacks #AITools