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

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

Most Retrieval-Augmented Generation (RAG) pipelines today stop at a single task — retrieve, generate, and respond. That model works, but it’s 𝗻𝗼𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. It doesn’t adapt, retain memory, or coordinate reasoning across multiple tools. That’s where 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗔𝗚 changes the game. 𝗔 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 In a traditional RAG setup, the LLM acts as a passive generator. In an 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 system, it becomes an 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗲𝗿 — supported by a network of specialized components that collaborate like an intelligent team. Here’s how it works: 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 — The decision-maker that interprets user intent and routes requests to the right tools or agents. It’s the core logic layer that turns a static flow into an adaptive system. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 — Maintains awareness across turns, retaining relevant context and passing it to the LLM. This eliminates “context resets” and improves answer consistency over time. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 — Divided into Short-Term (session-based) and Long-Term (persistent or vector-based) memory, it allows the system to 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Every interaction strengthens the model’s knowledge base. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗮𝘆𝗲𝗿 — The foundation. It combines similarity search, embeddings, and multi-granular document segmentation (sentence, paragraph, recursive) for precision retrieval. 𝗧𝗼𝗼𝗹 𝗟𝗮𝘆𝗲𝗿 — Includes the Search Tool, Vector Store Tool, and Code Interpreter Tool — each acting as a functional agent that executes specialized tasks and returns structured outputs. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 — Every user response feeds insights back into the vector store, creating a continuous learning and improvement cycle. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Agentic RAG transforms an LLM from a passive responder into a 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 capable of reasoning, memory, and self-optimization. This shift isn’t just technical — it’s strategic It defines how AI systems will evolve inside organizations: from one-off assistants to adaptive agents that understand context, learn continuously, and execute with autonomy.

The technology that powers Retrieval-Augmented Generation (RAG) apps behind the scenes isn’t such a mystery. This stack breaks it down for you, from LLMs to frameworks, databases, and data extraction. 1️⃣Large Language Models (LLMs) Core of any RAG app. Models like OpenAI, Claude, DeepSeek, Gemini, and Llama generate responses based on both prompts and retrieved data. 2️⃣Databases Tools like FAISS, Pinecone, Milvus, and Chroma store your vectorized knowledge - enabling fast, accurate search during retrieval. 3️⃣Retrieval + Embeddings Embeddings from tools like OpenAI, Nomic, or Cohere turn your content into vectors. These are matched and retrieved to feed into the LLMs. 4️⃣Frameworks & Model Access LangChain, LlamaIndex, Haystack, HuggingFace, and OpenRouter make it easy to chain components and access LLMs efficiently. 5️⃣Data Extraction Tools like Llamaparse, Docling, Megaparse, and Claude API extract useful info from PDFs, websites, and documents - boosting context quality. Building smarter AI apps with RAG in 2025? Save this stack and follow for more deep dives into AI workflows, model architectures, and real-world applications. #genai #artificialintelligence

🌟 This editorial from Radiology by Merel Huisman, MD, PhD, Felipe Kitamura, MD, PhD, Tessa Cook, Keith Hentel, Jonathan Elias, George Shih, and Linda Moy discusses the benefits and challenges of using large language models (LLMs) in clinical radiology, specifically focusing on clinical decision support, society guidelines and best practices, accuracy monitoring, academic administrative support, open-source and commercial LLMs, and agentic workflows. 🔍 It explores the potential of LLMs to enhance radiologists' work, highlighting their capabilities in generating reports, improving diagnostic accuracy, and providing patient-centered information. 🚨 The authors warn against overreliance on LLMs and the need for continuous monitoring, emphasizing the importance of maintaining accuracy and addressing biases. They advocate for combining quantitative metrics with qualitative user feedback. 💯 🤖 The article also explores the development of open-source LLMs, a potential solution to avoid overdependence on commercial LLMs, and the emerging field of agentic workflows, where LLMs can perform tasks and make decisions autonomously. 👍🏼 It's refreshing to see radiology finally discuss agentic AI. Overall, this editorial provides excellent insight into LLMs in radiology, highlighting not only their potential but also their pitfalls and limitations. It's a recommended read for anyone interested in LLMs in radiology. 🔗 to the editorial is in the first comment. 👇🏼 #Radiology #ArtificialIntelligence #LLMs #GenAI #AgenticAI Radiological Society of North America (RSNA) #RadiologyAI

In the inpatient setting, documentation remains one of the most burdensome tasks for #clinicians. While essential for continuity of care, H&Ps and discharge summaries are often delayed or left unsigned due to competing demands. As a Primary Care Physician #PCP, many of my recently discharged patients had their discharge summaries still unavailable or unsigned. This can disrupt post-discharge planning, delay medication reconciliation, and contribute to readmissions. A recent study in JAMA Internal Medicine evaluated whether large language models (#LLMs) can help. In a cross-sectional analysis of 100 inpatient encounters, the overall quality was comparable between LLM- and physician-generated notes (3.67 vs. 3.77; P = .21). However, the LLM summaries were more concise and coherent (4.01 vs. 3.70, P < .001; 4.16 vs. 4.01, P = .02), but less comprehensive (3.72 vs. 4.13, P < .001). Now the main issue we worry about is errors; interestingly enough, LLMs had more unique errors (2.91 vs. 1.82 per summary) yet similar low potential for harm compared to a physician-generated note (mean harm score: 0.84 vs. 0.36; P < .001). This suggests that LLMs can augment clinician #workflows by discharging #patients and ensuring continuity of care with the appropriate oversight. However, this raises an important concern: Reviewing LLM-generated content could itself become a new source of clinician burden!!! With LLMs capable of producing vast volumes of documentation, the oversight process must be stratified by risk scores or harm scores — using scoring mechanisms to flag summaries that require close attention while letting low-risk ones pass with minimal intervention. Without such an approach, we risk replacing one form of burnout with another. Link: https://lnkd.in/g-mpzzuw #HealthcareonLinkedin #LLM #AI #ClinicalInformatics #Physiciansburnout #Workflow #HealthIT #HealthInnovation

Small variations in prompts can lead to very different LLM responses. Research that measures LLM prompt sensitivity uncovers what matters, and the strategies to get the best outcomes. A new framework for prompt sensitivity, ProSA, shows that response robustness increases with factors including higher model confidence, few-shot examples, and larger model size. Some strategies you should consider given these findings: 💡 Understand Prompt Sensitivity and Test Variability: LLMs can produce different responses with minor rephrasings of the same prompt. Testing multiple prompt versions is essential, as even small wording adjustments can significantly impact the outcome. Organizations may benefit from creating a library of proven prompts, noting which styles perform best for different types of queries. 🧩 Integrate Few-Shot Examples for Consistency: Including few-shot examples (demonstrative samples within prompts) enhances the stability of responses, especially in larger models. For complex or high-priority tasks, adding a few-shot structure can reduce prompt sensitivity. Standardizing few-shot examples in key prompts across the organization helps ensure consistent output. 🧠 Match Prompt Style to Task Complexity: Different tasks benefit from different prompt strategies. Knowledge-based tasks like basic Q&A are generally less sensitive to prompt variations than complex, reasoning-heavy tasks, such as coding or creative requests. For these complex tasks, using structured, example-rich prompts can improve response reliability. 📈 Use Decoding Confidence as a Quality Check: High decoding confidence—the model’s level of certainty in its responses—indicates robustness against prompt variations. Organizations can track confidence scores to flag low-confidence responses and identify prompts that might need adjustment, enhancing the overall quality of outputs. 📜 Standardize Prompt Templates for Reliability: Simple, standardized templates reduce prompt sensitivity across users and tasks. For frequent or critical applications, well-designed, straightforward prompt templates minimize variability in responses. Organizations should consider a “best-practices” prompt set that can be shared across teams to ensure reliable outcomes. 🔄 Regularly Review and Optimize Prompts: As LLMs evolve, so may prompt performance. Routine prompt evaluations help organizations adapt to model changes and maintain high-quality, reliable responses over time. Regularly revisiting and refining key prompts ensures they stay aligned with the latest LLM behavior. Link to paper in comments.

I’ve spent the last year building and experiencing successful LLM use cases, and here’s where I’ve seen them create real impact: When LLM-based capabilities are embedded inside existing workflows. Chatbots are a great starting point, but here is how you can take them one step further: 1. Contract analysis that understands clauses, redlines, and legal context, reducing hours of manual review. 2. Co-pilots for internal tools, from writing SQL queries and generating Jira tickets to even composing HR emails, all inside your existing platforms. This is the shift: from flashy demos to practical, embedded intelligence that drives outcomes. P.S.: This is my 38th post in the '100 days of LLMs' series. Follow along to join this journey. #data #ai

🌟 Unlocking Cost-Effective AI for Healthcare 🌟 Exciting developments in leveraging LLMs at health system scale! A recent study published in npj Digital Medicine explores how LLMs like GPT-4-turbo-128k and Llama-3–70B can optimize clinical workflows while reducing costs. Key findings: 📋 Efficiency with Scale: By grouping multiple queries for clinical notes, costs dropped up to 17-fold while maintaining accuracy. ⚙️ Resilient Performance: High-capacity models handled up to 50 simultaneous tasks effectively, showcasing their robustness in complex medical scenarios. 💡 Clinical Potential: From generating patient summaries to improving hospital resource reports, LLMs can streamline operations across healthcare. 💰 The Challenge: High computational loads and costs are barriers, but strategies like query concatenation unlock new pathways for scalability. This research opens doors for safer, cost-efficient integration of LLMs into healthcare systems. The future of AI in medicine is not just about innovation but making it accessible and sustainable. 🔗 Read the full study for detailed insights as attached. #AIHealthcare #DigitalMedicine #LLMs #HealthTech #Innovation #GPT4 #HealthcareEfficiency #AIIntegration #MedicalAI #CostOptimization

Not surprisingly, at Mayfield Fund we are seeing a big wave of Gen AI applications; below are 5 use case themes emerging: 1. Content Generation: LLMs producing custom content for marketing, sales, and customer success, and also create multimedia for television, movies, games, and more. 2. Knowledge CoPilots: Offering on-demand expertise for better decision-making, LLMs act as the frontline for customer questions, aiding in knowledge navigation and synthesizing vast information swiftly. 3. Coding CoPilots: More than just interpretation, LLMs generate, refactor, and translate code. This optimizes tasks such as mainframe migration and comprehensive documentation drafting. 4. Coaching CoPilots: Real-time coaching ensuring decision accuracy, post-activity feedback from past interactions, and continuous actionable insights during tasks. 5. RPA Autopilots: LLM-driven robotic process automation that can automate entire job roles. What else are we missing?