Managing Specialized LLM Workflows for AI Projects

If you’re an AI engineer, product builder, or researcher- understanding how to specialize LLMs for domain-specific tasks is no longer optional. As foundation models grow more capable, the real differentiator will be: how well can you tailor them to your domain, use case, or user? Here’s a comprehensive breakdown of the 3-tiered landscape of Domain Specialization of LLMs. 1️⃣ External Augmentation (Black Box) No changes to the model weights, just enhancing what the model sees or does. → Domain Knowledge Augmentation Explicit: Feeding domain-rich documents (e.g. PDFs, policies, manuals) through RAG pipelines. Implicit: Allowing the LLM to infer domain norms from previous corpora without direct supervision. → Domain Tool Augmentation LLMs call tools: Use function calling or MCP to let LLMs fetch real-time domain data (e.g. stock prices, medical info). LLMs embodied in tools: Think of copilots embedded within design, coding, or analytics tools. Here, LLMs become a domain-native interface. 2️⃣ Prompt Crafting (Grey Box) We don’t change the model, but we engineer how we interact with it. → Discrete Prompting Zero-shot: The model generates without seeing examples. Few-shot: Handpicked examples are given inline. → Continuous Prompting Task-dependent: Prompts optimized per task (e.g. summarization vs. classification). Instance-dependent: Prompts tuned per input using techniques like Prefix-tuning or in-context gradient descent. 3️⃣ Model Fine-tuning (White Box) This is where the real domain injection happens, modifying weights. → Adapter-based Fine-tuning Neutral Adapters: Plug-in layers trained separately to inject new knowledge. Low-Rank Adapters (LoRA): Efficient parameter updates with minimal compute cost. Integrated Frameworks: Architectures that support multiple adapters across tasks and domains. → Task-oriented Fine-tuning Instruction-based: Datasets like FLAN or Self-Instruct used to tune the model for task following. Partial Knowledge Update: Selective weight updates focused on new domain knowledge without catastrophic forgetting. My two cents as someone building AI tools and advising enterprises: 🫰 Choosing the right specialization method isn’t just about performance, it’s about control, cost, and context. 🫰 If you’re in high-risk or regulated industries, white-box fine-tuning gives you interpretability and auditability. 🫰 If you’re shipping fast or dealing with changing data, black-box RAG and tool-augmentation might be more agile. 🫰 And if you’re stuck in between? Prompt engineering can give you 80% of the result with 20% of the effort. Save this for later if you’re designing domain-aware AI systems. Follow me (Aishwarya Srinivasan) for more AI insights!

❌ "𝗝𝘂𝘀𝘁 𝘂𝘀𝗲 𝗖𝗵𝗮𝘁𝗚𝗣𝗧" 𝗶𝘀 𝘁𝗲𝗿𝗿𝗶𝗯𝗹𝗲 𝗮𝗱𝘃𝗶𝗰𝗲. Here's what most AI & Automation leaders get wrong about LLMs: They're building their entire AI infrastructure around ONE or TWO models. The reality? There is no single "best LLM." The top models swap positions every few months, and each has unique strengths and costly blindspots. I analyzed the 6 frontier models driving enterprise AI today. Here's what I found: 𝟭. 𝗚𝗲𝗺𝗶𝗻𝗶 (𝟯 𝗣𝗿𝗼/𝗨𝗹𝘁𝗿𝗮) ✓ Superior reasoning and multimodality ✓ Excels at agentic workflows ✗ Not useful for writing tasks 𝟮. 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 (𝗚𝗣𝗧-𝟱) ✓ Most reliable all-around ✓ Mature ecosystem ✗ A lot prompt-dependent 𝟯. 𝗖𝗹𝗮𝘂𝗱𝗲 (𝟰.𝟱 𝗦𝗼𝗻𝗻𝗲𝘁/𝗢𝗽𝘂𝘀) ✓ Industry leader in coding & debugging ✓ Enterprise-grade safety ✗ Opus is very expensive 𝟰. 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 (𝗩𝟯.𝟮-𝗘𝘅𝗽) ✓ Great cost-efficiency ✓ Top-tier coding and math ✗ Less mature ecosystem 𝟱. 𝗚𝗿𝗼𝗸 (𝟰/𝟰.𝟭) ✓ Real-time data access ✓ High-speed querying ✗ Limited free access 𝟲. 𝗞𝗶𝗺𝗶 𝗔𝗜 (𝗞𝟮 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴) ✓ Massive context windows ✓ Superior long document analysis ✗ Chinese market focus The winning strategy isn't picking one. It's orchestration. Here's the playbook: → Stop hardcoding single-vendor APIs → Route code writing & reviews to Claude → Send agentic & multimodal workflows to Gemini → Use DeepSeek for cost-effective baseline tasks → Build multi-step workflows, not one-shot prompts 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲? Your competitive advantage isn't choosing the "best" model. It's building orchestration systems that route intelligently across all of them. The future of enterprise automation is agentic systems that manage your LLM landscape for you. What's the LLM strategy that's working for you? ---- 🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX Repost if this helped you see the shift ♻️

When working with multiple LLM providers, managing prompts, and handling complex data flows — structure isn't a luxury, it's a necessity. A well-organized architecture enables: → Collaboration between ML engineers and developers → Rapid experimentation with reproducibility → Consistent error handling, rate limiting, and logging → Clear separation of configuration (YAML) and logic (code) 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗧𝗵𝗮𝘁 𝗗𝗿𝗶𝘃𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 It’s not just about folder layout — it’s how components interact and scale together: → Centralized configuration using YAML files → A dedicated prompt engineering module with templates and few-shot examples → Properly sandboxed model clients with standardized interfaces → Utilities for caching, observability, and structured logging → Modular handlers for managing API calls and workflows This setup can save teams countless hours in debugging, onboarding, and scaling real-world GenAI systems — whether you're building RAG pipelines, fine-tuning models, or developing agent-based architectures. → What’s your go-to project structure when working with LLMs or Generative AI systems? Let’s share ideas and learn from each other.

Most LLM-infused RAG-based vector DB searches could be done just as easily with SQL or SPARQL queries. We must stop overengineering workflows just to get the agentic label. All that accomplishes is making products more complex and expensive. Use the simplest approach to create the outcome, and more use cases will be feasible. Use AI as an augmentation layer when reinventing workflows. Remember, in an agentic workflow, not every step must be handled by an LLM. The power of agents is their ability to use tools like people do. Leverage tools, information architecture, and other models to keep costs down. I had a startup founder ask about building a recommendation system with LLMs yesterday. In ten minutes, I helped them save millions in AI API costs by working through a simple workflow evaluation with them. We discovered that Step 1 was the only place that an LLM could provide value. They wanted to enable users to make their requests with natural language, so the app could extract more information to use for personalization. Intent detection, multistep conversation (asking clarifying questions), and gathering granular information from unstructured data are all great LLM use case categories. What happens next doesn’t have to use expensive models. The value creation came from reinventing the user workflow, not the operational workflow. A menu-based or categorical selection workflow isn’t feasible for this use case due to the complexity of the subject domain. The only way to extract all the information required to serve a high-quality recommendation was conversationally. The rest of the workflow was fairly straightforward. No vector DBs or RAG required. Once all the details were extracted, the rest of the workflow fit into conditional statements and logical steps. That’s the power of AI workflow evaluations. It’s just as valuable to define what AI shouldn’t do to make the unit economics work for more use cases. The upfront work to make the data more structured and define the logic could also benefit from an LLM’s help. That one-time cost can save the business from having to use an LLM repeatedly as part of the product’s workflow. Just because we could use AI doesn’t mean we should. I advise clients to run workflow evaluations and only use AI when it provides more value than an alternative, lower-cost approach.

Building LLM Agent Architectures on AWS - The Future of Scalable AI Workflows What if you could design AI agents that not only think but also collaborate, route tasks, and refine results automatically? That’s exactly what AWS’s LLM Agent Architecture enables. By combining Amazon Bedrock, AWS Lambda, and external APIs, developers can build intelligent, distributed agent systems that mirror human-like reasoning and decision-making. These are not just chatbots - they’re autonomous, orchestrated systems that handle workflows across industries, from customer service to logistics. Here’s a breakdown of the core patterns powering modern LLM agents : Breakdown: Key Patterns for AI Workflows on AWS 1. Prompt Chaining / Saga Pattern Each step’s output becomes the next input — enabling multi-step reasoning and transactional workflows like order handling, payments, and shipping. Think of it as a conversational assembly line. 2. Routing / Dynamic Dispatch Pattern Uses an intent router to direct queries to the right tool, model, or API. Just like a call center routing customers to the right department — but automated. 3. Parallelization / Scatter-Gather Pattern Agents perform tasks in parallel Lambda functions, then aggregate responses for efficiency and faster decisions. Multiple agents think together — one answer, many minds. 4. Saga / Orchestration Pattern Central orchestrator agents manage multiple collaborators, synchronizing tasks across APIs, data sources, and LLMs. Perfect for managing complex, multi-agent projects like report generation or dynamic workflows. 5. Evaluator / Reflect-Refine Loop Pattern Introduces a feedback mechanism where one agent evaluates another’s output for accuracy and consistency. Essential for building trustworthy, self-improving AI systems. AWS enables modular, event-driven, and autonomous AI architectures, where each pattern represents a step toward self-reliant, production-grade intelligence. From prompt chaining to reflective feedback loops, these blueprints are reshaping how enterprises deploy scalable LLM agents. #AIAgents

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

Have you observed lately that many agentic AI applications fail because they rely directly on raw LLM calls without a gateway to handle context routing, model orchestration, caching, rate-limiting, and fallback strategies? You must need an LLM gateway or a layer of such kind that acts as a middleware layer that sits between your application and multiple LLM providers. Hence, an LLM gateway is essential for building scalable, safe, and cost-effective agentic AI applications in the enterprise. An LLM gateway essentially functions as a central control panel to orchestrate workloads across models, agents, and MCP servers (the emerging protocol connecting AI agents to external services). Core functions and concepts of an LLM gateway include: ➤ Unified Entry Point: It provides a single, consistent interface (API) for applications to interact with multiple foundational model providers. ➤ Abstraction Layer: It hides the complexity and provider-specific quirks of working directly with individual LLM APIs. This means developers can use the same code structure regardless of which model they call. ➤ Traffic Controller: It intelligently routes requests to the most suitable LLM based on specific criteria like performance, cost, or policy. ➤ Orchestration Platform: It improves the deployment and management of LLMs in production environments by handling security, authentication, and model updates from a single platform. LLM gateways are becoming essential, particularly for enterprises building production-ready and scalable agentic AI applications, because they address multidimensional challenges related to vendor lock-in, complexity, costs, security, and reliability. Know more about LLM gateways through below resources: https://lnkd.in/gimgJ4hD https://lnkd.in/gawvkzGw https://lnkd.in/g-377ESP

Less talking, more building: 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗦𝘆𝘀𝘁𝗲𝗺 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵! 👇 Some weeks ago I released an episode of my Newsletter and an update to the 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 GitHub repository. There I implemented a Deep Research Agent from scratch without using any LLM Orchestration frameworks (using DeepSeek-R1 for some planning tasks). In the project we implement the following Agentic topology: 𝟭. A user provides a query or topic to be researched. 𝟮. A LLM creates an outline of the final report that it will be aiming for. It will be instructed to produce not more than a certain number of paragraphs. 𝟯. Each of the paragraph description is fed into a research process separately to produce a comprehensive set of information to be used in report construction. Detailed description of the research process will be outlined in the next section. 𝟰. All of the information will be fed into a summarisation step that will construct the final report including conclusion. 𝟱. The report will then be delivered to the user in MarkDown form. Each of the research steps are following given flow: 𝟭. Once we have the outline of each paragraph, it will be passed to a LLM to construct Web Search queries in an attempt to best enrich the information needed. 𝟮. The LLM will output the search query and the reasoning behind it. 𝟯. We will execute Web search against the query and retrieve top relevant results. 𝟰. The results will be passed to the Reflection step where a LLM will reason about any missed nuances to try and come up with a search query that would enrich the initial results. 𝟱. This process will be repeated for n times in an attempt to get the best set of information possible. Detailed walkthrough Blog Post: https://lnkd.in/dWMapU7r GitHub with implementation code and Notebooks to follow: https://lnkd.in/daiAbpc9 Happy Building! Be sure to leave a like or star if you find the content useful! #AI #LLM

You don’t need to be an AI agent to be agentic. No, that’s not an inspirational poster. It’s my research takeaway for how companies should build AI into their business. Agents are the equivalent of a self-driving Ferrari that keeps driving itself into the wall. It looks and sounds cool, but there is a better use for your money. AI workflows offer a more predictable and reliable way to sound super cool while also yielding practical results. Anthropic defines both agents and workflows as agentic systems, specifically in this way: 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: systems where predefined code paths orchestrate the use of LLMs and tools 𝗔𝗴𝗲𝗻𝘁𝘀: systems where LLMs dynamically decide their own path and tool uses For any organization leaning into Agentic AI, don’t start with agents. You will just overcomplicate the solution. Instead, try these workflows from Anthropic’s guide to effectively building AI agents: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁-𝗰𝗵𝗮𝗶𝗻𝗶𝗻𝗴:  The type A of workflows, this breaks a task down into sequential tasks organized and logical steps, with each step building on the last. It can include gates where you can verify the information before going through the entire process. 𝟮. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: The multi-tasker workflow, this separates tasks across multiple LLMs and then combines the outputs. This is great for speed, but also collects multiple perspectives from different LLMs to increase confidence in the results. 𝟯. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: The task master of workflows, this breaks down complex tasks into different categories and assigns those to specialized LLMs that are best suited for the task. Just like you don’t want to give an advanced task to an intern or a basic task to a senior employee, this find the right LLM for the right job. 𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝘄𝗼𝗿𝗸𝗲𝗿𝘀: The middle manager of the workflows, this has an LLM that breaks down the tasks and delegates them to other LLMs, then synthesizes their results. This is best suited for complex tasks where you don’t quite know what subtasks are going to be needed. 𝟱. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: The peer review of workflows, this uses an LLM to generate a response while another LLM evaluates and provides feedback in a loop until it passes muster. View my full write-up here: https://lnkd.in/eZXdRrxz

Your LLM app feels buggy, but you can't pinpoint why. On Lenny Rachitsky's podcast, Shreya Shankar and I broke down the solution: a systematic AI evaluation workflow. Here is the workflow we teach thousands of engineers and PMs, including those at OpenAI, Google, Meta and others: 1. Open coding: Manually review traces and write notes on failure modes (e.g., hallucinations, poor handoffs, janky flows) 2. Axial coding: Use LLMs to cluster those notes into concrete, repeatable failure types 3. Prioritize with data: Do data analysis to understand which issues happen most and which are most severe. 4. Automated evaluators: Build code-based evals (e.g., JSON formatting, tool call correctness) or LLM-as-judge (e.g., “Did the agent fail to escalate when it should?”) 5. Run your evals in CI/CD and in production monitoring to catch regressions and discover issues. Many teams skip this. They ship prompts, see weird behavior, and guess at the root cause. That guesswork doesn’t scale. Evals make that guesswork go away. They turn requirements into executable specs, constantly validating whether your agent is behaving the way you expect. If you’d like to demystify the process of developing effective evals and learn techniques to improve your AI product, you can join our next Maven cohort on October 6: http://bit.ly/4pDmoiV