LLM Financial Applications

If you’re building anything with LLMs, your system architecture matters more than your prompts. Most people stop at “call the model, get the output.” But LLM-native systems need workflows, blueprints that define how multiple LLM calls interact, how routing, evaluation, memory, tools, or chaining come into play. Here’s a breakdown of 6 core LLM workflows I see in production: 🧠 LLM Augmentation Classic RAG + tools setup. The model augments its own capabilities using: → Retrieval (e.g., from vector DBs) → Tool use (e.g., calculators, APIs) → Memory (short-term or long-term context) 🔗 Prompt Chaining Workflow Sequential reasoning across steps. Each output is validated (pass/fail) → passed to the next model. Great for multi-stage tasks like reasoning, summarizing, translating, and evaluating. 🛣 LLM Routing Workflow Input routed to different models (or prompts) based on the type of task. Example: classification → Q&A → summarization all handled by different call paths. 📊 LLM Parallelization Workflow (Aggregator) Run multiple models/tasks in parallel → aggregate the outputs. Useful for ensembling or sourcing multiple perspectives. 🎼 LLM Parallelization Workflow (Synthesizer) A more orchestrated version with a control layer. Think: multi-agent systems with a conductor + synthesizer to harmonize responses. 🧪 Evaluator–Optimizer Workflow The most underrated architecture. One LLM generates. Another evaluates (pass/fail + feedback). This loop continues until quality thresholds are met. If you’re an AI engineer, don’t just build for single-shot inference. Design workflows that scale, self-correct, and adapt. 📌 Save this visual for your next project architecture review. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

LLMs massively empower individuals. Used well, they augment thinking and intentions to an extraordinary degree. The impact is far more muted and delayed for large organizations, which have entrenched ways of working that will take years to shift through careful negotiation of culture and governance. AI doyen Andrej Karpathy has neatly laid out how genAI results, quite simply, in: Power to the people. Transformative technologies have usually been developed and used by governments and the military, and then diffused to companies and individuals. For LLMs, everyone has access to the same quality AI, largely free, in every language, to be applied immediately to whatever users want to do. In contrast, there are many reasons why it will be far slower for organizations to get value: ➡️ LLMs offer broad but shallow capabilities, which are less valuable to organizations already equipped with deep domain experts. ➡️ Organizations already consolidate specialized expertise, so LLMs typically enhance existing workflows rather than enabling entirely new capabilities. ➡️ The improvements LLMs provide are incremental, making organizations slightly more efficient at tasks they already perform well. ➡️ Integrating LLMs into complex legacy systems and existing processes is technically challenging and resource-intensive. ➡️ Strict security, privacy, and regulatory requirements limit how freely LLMs can be used in corporate and government environments. ➡️ The risk of errors or hallucinations from LLMs is unacceptable in high-stakes or legally sensitive organizational contexts. ➡️ Organizational culture can resist the adoption of new tools, especially when they disrupt established roles or processes. ➡️ Decision-making in large organizations is often slow, with multiple layers of approval and governance slowing experimentation. ➡️ Retraining employees to use LLMs effectively at scale is a significant undertaking with cost and coordination challenges. ➡️ Bureaucracy, turf wars, and political dynamics within organizations often create resistance to rapid technological adoption. Take advantage of power flowing to the people!

One of the most interesting debates I heard today at HIMSS... “Is it time to trust LLMs over interoperability standards?” For decades, healthcare IT has tried to solve interoperability the same way: Build structured standards → translate data → move it between systems of record. FHIR. HL7. Interfaces. Mapping tables. Endless implementation cycles. But the panel posed a provocative idea: What if LLMs flip that model entirely? Instead of forcing every piece of healthcare data into rigid structure… 👉 Let LLMs work directly with the messy, unstructured reality of healthcare. #RaihanReacts to some themes that stood out: 1. LLMs make massive EHR outputs digestible One panelist joked that they “spent yesterday afternoon in Claude Code building their own apps.” That comment actually reflects a real shift happening across healthcare IT. Clinicians and operators are suddenly able to take: • thousands of lines of chart data • referral notes • discharge summaries • scanned PDFs • messy clinical documentation …and have LLMs summarize, extract, and explain it in seconds. The result: 👉 EHR data becomes understandable and actionable, not just stored. 2. Unstructured data may actually be healthcare’s natural format Healthcare has always struggled to structure everything. But maybe that was the wrong goal. Clinical care is inherently messy: • free-text notes • patient narratives • imaging reports • external records • faxes (still…) LLMs are the first tech that actually thrives in this environment. Instead of forcing the system to change…the tech adapts to healthcare’s complexity. 3. Accuracy and governance are the real challenge Of course, trusting LLMs introduces a critical question: How do we verify the outputs? Several panelists talked about the need for: • monitoring frameworks • validation layers • auditability of summaries • “toggle switches” to turn LLM workflows on or off 👉 This is going to be one of the major operational challenges of AI adoption in healthcare. Not whether models can summarize data. But whether orgs can safely trust those summaries at scale. 4. Early operational wins are already emerging A few examples discussed: • Faster prescription workflows • Referral turnaround improvements • Automated chart summarization for intake and review These are small changes individually. But across a health system, they add up to thousands of hours of operational efficiency. My take: - Interoperability standards are not going away. - Systems of record will still matter. - But LLMs may become the universal interface layer that sits on top of them. HIMSS Global Health Conference & Exhibition

LLMs force a shift from performing work to specifying work. Because the machine only responds to what you articulate, you end up writing constantly: intent, constraints, context, expected output. The act is functional rather than literary. You strip away tone, polish and performance. What remains is the core of knowledge work: clarifying the problem, defining the goal, summoning the factual context. The immediacy of feedback is captivating. You see instantly how a clearer description yields a better result. Over time this trains the fundamental skills of high-leverage white-collar work: documenting ideas, setting objectives, defining deliverables, and enriching the brief with the right context. A generation now growing up with this muscle memory will expect work to run this way. Their default workflow will be: capture the idea in writing, specify the task, let the machine execute or assist. Meetings without agendas, projects without specs, assignments without context will feel amateurish. The norms of today’s office—already bloated by vague communication and performative collaboration—will look as dated as the mid-century rituals of secretaries typing dictated letters and executives spending afternoons in performative chatter over drinks. The shift is not that AI replaces office work; it rewires the discipline of doing it.

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 transforming how data teams tackle everyday tasks. Building ETLs, running analyses, statistical inference, and training ML models. The results are genuinely impressive. Some see this as a threat to their roles, and that concern is valid. It is still too early to tell exactly what all of this means for data teams. But I think that all of these systems still need a skilled professional at the center guiding them. We are simply just removing the painful, repetitive grunt work that used to define a data person's day-to-day work. It feels like data work is actually becoming what it was billed as ten years ago when I first got into it: the "sexiest job of the 21st century." Now professionals can finally focus on what actually matters: strategy, insights, and real business impact instead of wrestling with Airflow configs and debugging SQL for hours. What this means in practice is that data teams have dramatically increased their velocity. They're shipping analyses, dashboards, and insights at the speed the business actually moves. No more waiting weeks for a simple report. When a business question comes in, the data team can answer it now, not next sprint. That's a fundamental shift in how data teams operate and how valuable they actually are to the business.

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?

LLMs are going vertical → and functional. We’re moving from “everyday AI” to functional AI: domain-specific agents embedded in real workflows where enterprise value is trapped Proof the shift is led by LLM providers themselves: Banking: OpenAI is partnering directly with banks (e.g., BNY Mellon’s multiyear deal to upgrade its Eliza platform; NatWest’s UK-first collaboration). These are not generic chats, they’re deeply embedded, regulated-industry builds. Life sciences: Anthropic’s Claude for Life Sciences adds connectors to tools like Benchling, PubMed, 10x Genomics and offers domain skills: from protocol QA to bioinformatics workflows. That’s vertical by design. Healthcare: Google’s MedLM + Vertex AI Search for Healthcare targets clinical documentation and medical record retrieval: out-of-the-box isn’t enough; it’s workflow-native. Industrial: Siemens Industrial Copilot (with Microsoft) is scaling across factories and engineering teams. LLMs tuned to PLC code, Teamcenter, and shop-floor realities. The takeaway: The real value isn’t a model. It is configuration and customization: grounding in your systems of record, domain ontologies, governed connectors, policy guardrails, eval harnesses tied to domain KPIs, and change management. Off-the-shelf chat interface won’t clear the bar for accuracy, compliance, or UX in complex functions. Verticalization is the on-ramp. Customization is the unlock. #EnterpriseAI 

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