Tasks You Can Automate With LLMs

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.

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.

Don't ask an LLM to do your evals. Instead, use it to accelerate them. LLMs can speed up parts of your eval workflow, but they can’t replace human judgment where your expertise is essential. Here are some areas where LLMs can help: 1. First-pass axial coding: After you’ve open coded 30–50 traces yourself, use an LLM to organize your raw failure notes into proposed groupings. This helps you quickly spot patterns, but always review and refine the clusters yourself. Note: If you aren’t familiar with axial and open coding, see this faq: https://lnkd.in/gpgDgjpz 2. Mapping annotations to failure modes: Once you’ve defined failure categories, you can ask an LLM to suggest which categories apply to each new trace (e.g., “Given this annotation: [open_annotation] and these failure modes: [list_of_failure_modes], which apply?”). 3. Suggesting prompt improvements: When you notice recurring problems, have the LLM propose concrete changes to your prompts. Review these suggestions before adopting any changes. 4. Analyzing annotation data: Use LLMs or AI-powered notebooks to find patterns in your labels, such as “reports of lag increase 3x during peak usage hours” or “slow response times are mostly reported from users on mobile devices.” However, you shouldn’t outsource these activities to an LLM: 1. Initial open coding: Always read through the raw traces yourself at the start. This is how you discover new types of failures, understand user pain points, and build intuition about your data. Never skip this or delegate it. 2. Validating failure taxonomies: LLM-generated groupings need your review. For example, an LLM might group both “app crashes after login” and “login takes too long” under a single “login issues” category, even though one is a stability problem and the other is a performance problem. Without your intervention, you’d miss that these issues require different fixes. 3. Ground truth labeling: For any data used for testing/validating LLM-as-Judge evaluators, hand-validate each label. LLMs can make mistakes that lead to unreliable benchmarks. 4. Root cause analysis: LLMs may point out obvious issues, but only human review will catch patterns like errors that occur in specific workflows or edge cases—such as bugs that happen only when users paste data from Excel. Start by examining data manually to understand what’s going wrong. Use LLMs to scale what you’ve learned, not to avoid looking at data. Read this and other eval tips here: https://lnkd.in/gfUWAjR3

𝗧𝗼𝗽 𝟵 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 Most people think AI = prompt → response. But real AI systems are built using workflows, not just single prompts. These workflows define how LLMs: • break problems • reason step-by-step • use tools • collaborate • improve outputs Understanding these is key to building real AI agents. Here is a simple breakdown. 1. Prompt Chaining Break a task into multiple steps where each LLM call builds on the previous one. Used for: • chatbots • multi-step reasoning • structured workflows 2. Parallelization Run multiple LLM calls at the same time and combine results. Used for: • faster processing • evaluations • handling multiple inputs 3. Orchestrator–Worker A central LLM splits tasks and assigns them to smaller worker models. Used for: • agentic RAG • coding agents • complex task delegation 4. Evaluator–Optimizer One model generates output, another evaluates and improves it in a loop. Used for: • data validation • improving response quality • feedback-based systems 5. Router Classifies input and sends it to the right workflow or model. Used for: • customer support systems • multi-agent setups • intelligent routing 6. Autonomous Workflow The agent interacts with tools and environment, learns from feedback, and continues execution. Used for: • autonomous agents • real-world task execution 7. Reflexion The model reviews its own output and improves it iteratively. Used for: • complex reasoning • debugging tasks • self-correcting systems 8. ReWOO Separates planning and execution. One part plans tasks, others execute them. Used for: • deep research • multi-step problem solving 9. Plan and Execute The agent creates a plan, executes steps, and updates based on results. Used for: • business workflows • automation pipelines 💡 Simple mental model • Chaining → step-by-step thinking • Parallel → faster execution • Orchestrator → task distribution • Evaluator → quality improvement • Router → smart decision-making • Autonomous → self-running systems 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Moving from: single prompts → structured workflows is what turns: LLMs → real AI systems Most people are still at the prompt level. The real power comes from designing workflows. Which workflow are you using the most right now? Image credits: Rakesh Gohel #AI #AIAgents #LLM #AgenticAI #GenAI #AIEngineering #Automation

Getting LLMs to perform actionable tasks is one of the biggest collective challenges around using AI. But with function calling, AI can be more actionable. Function calling can act as the bridge between conversation and action for AI. With function calling, the LLM can “call” specific, defined functions within a system to deliver specific—and, if desired, actionable—outcomes. For example, function calling enables an LLM to look up a specific zip code and return structured data when asked, “What’s the weather in XYZ?” Now, apply that to something more complex, like “Build me a recommendation for my ad campaign budgets.” Because the LLM knows “set campaign budget,” as a function, it returns structured data that can include things like the campaign ID and budget value.  This lends itself to this whole new world of operating your software from an AI agent. 💡 AI can do more than just get a conversation going. It can become your operating software. The potential in this is absolutely enormous. So far, we’ve defined about 30 routine tasks within Fluency’s system that can be completed with functional calling in Muse AI, our system-specific AI agent. This means you can use AI within Fluency to: - Manage keywords and targeting (including exclusions) - Design campaign structure - Rotate static messaging on a set cadence - Manage specials and promotions in ad copy - Generate performance reports for every client in your portfolio - Recommend (and make) budget adjustments Function calling means AI doesn’t just talk to you—it works FOR you. And we’re just scratching the surface of what’s possible.

New research introduces IntelEX. This paper shows how LLMs can help automate the pipeline from unstructured threat reports to detection rules, reducing the boring and error-prone workflow for security analysts. Testing on 1,769 newly crawled reports, it identified 3,591 attack techniques and includes a LLM Judge module to reduce hallucinations. Some tips we can borrow from the paper: - when processing CTI reports with LLMs, chunk documents by linking sentences around IoCs rather than feeding the full document - used a vector DB of MITRE ATT&CK tactics/techniques for retrieval - used separate LLM instances for extraction, retrieval, and verification to avoid context contamination (looks like they deliberately did not pass context from previous steps downstream) - step to explicitly reason about why a detection/classification is valid or invalid - extra steps to improve semantic understanding of threat intel by using attack variant generation and attack re-construction - the authors found GPT-4-mini achieved similar accuracy to GPT-4 at 1/20th the cost. IntelEX: A LLM-driven Attack-level Threat Intelligence Extraction Framework: 🔗 https://lnkd.in/eH9tkgbt