Comparing LLM Development and Industrial AI Deployment

𝗟𝗟𝗠 𝘃𝘀. 𝗥𝗔𝗚 𝘃𝘀. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 𝘃𝘀. 𝗔𝗴𝗲𝗻𝘁 𝘃𝘀. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 — 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝘄𝗵𝗮𝘁 I keep getting one question from teams building with GenAI: Which approach should we choose? This one-pager visual breaks down the trade-offs. Below is the practical guide I use on real projects. 𝟭) 𝗟𝗟𝗠 What it is: Prompt → model → answer. Use when: General knowledge, ideation, drafting, small utilities. Watch out for: Hallucinations on domain-specific facts; limited to model’s pretraining. 𝟮) 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) What it is: Query → retrieve context from a knowledge base → feed context + query to LLM → grounded answer. Model weights don’t change. Use when: You have proprietary docs, policies, catalogs, tickets, or logs that change frequently. Benefits: Lower cost than training, auditable sources, fast updates. Key tips: Good chunking, embeddings, metadata, and re-ranking determine quality more than the LLM choice. 𝟯) 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 What it is: Train the model on input→output pairs to change its weights (LLM → LLM′). Use when: You need consistent style, domain tone, or task-specific behavior (classification, templated replies, structured outputs). Benefits: Lower prompt complexity, stable behavior, smaller inference tokens. Caveats: Needs clean, labeled data; versioning and evaluation are critical. 𝟰) 𝗔𝗴𝗲𝗻𝘁 What it is: LLM + memory + tools/APIs with a think → act → observe loop. Use when: Tasks require multi-step reasoning, tool use (search, SQL, APIs), or state over time. Examples: Troubleshooting flows, data enrichment, workflow automation. Risks: Loops, tool misuse, latency. Use guardrails, timeouts, and action limits. 𝟱) 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 (𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀) What it is: Coordinated roles (planner, executor, critic) that plan → act → observe → learn from feedback. Use when: Complex processes with decomposition, review, and collaboration across specialized agents. Examples: Customer ops copilots, multi-step ETL with validation, enterprise workflows spanning multiple systems. Challenges: Orchestration, determinism, monitoring, and cost control. Metrics that matter Grounding: citation hit-rate, answer verifiability (RAG) Quality: task accuracy, pass@k, error rate Efficiency: latency, tokens, cost per resolution Safety: hallucination rate, tool misuse, policy violations Reliability: determinism, replayability, test coverage Design Tips: Start with RAG before touching fine-tuning; data beats weights early on. Keep prompts short; push knowledge to the retriever or the dataset. Add evaluation harnesses from day one (gold sets, unit tests for prompts/tools). Log everything: context windows, actions, failures, and human overrides. Treat agents like software: versioning, guardrails, circuit breakers, and audits.

I started by asking AI to do everything. Six months later, 65% of my agent’s workflow nodes run as non-AI code. The first version was fully agentic : every task went to an LLM. LLMs would confidently progress through tasks, though not always accurately. So I added tools to constrain what the LLM could call. Limited its ability to deviate. I added a Discovery tool to help the AI find those tools. Better, but not enough. Then I found Stripe’s minion architecture. Their insight : deterministic code handles the predictable ; LLMs tackle the ambiguous. I implemented blueprints, workflow charts written in code. Each blueprint specifies nodes, transitions between them, trigger conditions for matching tasks, & explicit error handling. This differs from skills or prompts. A skill tells the LLM what to do. A blueprint tells the system when to involve the LLM at all. Each blueprint is a directed graph of nodes. Nodes come in two types : deterministic (code) & agentic (LLM). Transitions between nodes can branch based on conditions. Deal pipeline updates, chat messages, & email routing account for 29% of workflows, all without a single LLM call. Company research, newsletter processing, & person research need the LLM for extraction & synthesis only. Another 36%. The workflow runs 67-91% as code. The LLM sees only what it needs : a chunk of text to summarize, a list to categorize, processed in one to three turns with constrained tools. Blog posts, document analysis, bug fixes are genuinely hybrid. 21% of workflows. Multiple LLM calls iterate toward quality. Only 14% remain fully agentic. Data transforms & error investigations. These tend to be coding tasks rather than evaluating a decision point in a workflow. The LLM needs freedom to explore. AI started doing everything. Now it handles routing, exceptions, research, planning, & coding. The rest runs without it. Is AI doing less? Yes. Is the system doing more? Also yes. The blueprints, the tools, the skills might be temporary scaffolding. With each new model release, capabilities expand. Tasks that required deterministic code six months ago might not tomorrow.

The wave of LLM lab services JVs is a pretty big admission that deployment is the hard part. Deployment has always been ‘someone else's problem’ in the lab worldview. Train the model, release the API, figure out enterprise adoption later (ideally with a consulting partner's name attached) The LLM labs have never had to actually make their tech work inside a real organization, which is fine for coding, but doesn't work when the use case is "transform how Unilever runs its content operations" or "rewire how Vodafone handles regulatory compliance across 20 markets." But here's the thing we've learned after hundreds of successful implementations in the Global 2000. Services and product go hand in hand. The only way to get AI to actually work in complex enterprise environments is to productize the feedback loop. To take what you learn optimizing for one customer and systematically build it back into the platform for them and all customers. Customer by customer, you eventually productize harnesses for agents for every micro use case. We've chosen to do this for enterprise sales and marketing. And that's product discipline, not professional services. Bc real outcomes in AI require — • workflows pre-built for how complex knowledge work happens • governance that's native — policies for connectors, built-in guardrails, regulations live and baked in, not a compliance layer you staple on afterward • models optimized for cost and performance by customer that keep up w frontier models, not fall behind • brand standards / skills that are centrally controlled / apply to all AI use, with robust lifecycle management Neither the labs or the services firms are building what enterprises actually need — a platform that treats deployment as a first-class product problem and not a professional services revenue line. The consulting firms are quietly furious about the services JV land grab. They built practices around being that implementation layer. But the frustration misses the real point — enterprises want to own their own transformations, not rent them.

Industrial Foundation Models: The Missing Piece for Industrial AI? Large Language Models like ChatGPT, Gemini and Claude have been trained on vast amounts of publicly available internet data. They excel at general-purpose tasks. But here is the problem: industrial data is nothing like internet data. The data that matters most for industrial AI, proprietary engineering artifacts, sensor signals, simulation results, production logs, sits behind corporate firewalls. It is a company's most valuable intellectual property. But this creates a fundamental barrier: the models that are best at understanding language have never seen the data that drives manufacturing, engineering and operations. This is why Industrial Foundation Models are emerging as a dedicated research and development focus. The idea is straightforward but ambitious: train models on large collections of industrial data so they can understand and process the formats that general-purpose LLMs have never been exposed to: time series, CAD geometry, CAE simulation data, structured tabular formats. These are the languages of industry and no commercial LLM speaks them fluently today. The application areas are broad and consequential. In engineering, Industrial Foundation Models are being developed to generate and link RFLP artifacts, interpret CAD files and transform BOM structures. Tasks that currently require deep domain expertise and significant manual effort. In robotics, foundation models are applied to trajectory planning, object detection and control, moving from perception to autonomous decision-making in physical environments. In manufacturing, these models enable predictive maintenance and condition monitoring, shifting from reactive to anticipatory operations. And in process industries, they are used for process optimization and control, where even marginal improvements translate to significant cost savings at scale. The challenge is real: industrial data is fragmented, proprietary, and comes in formats that were never designed for machine learning. But the companies and research groups that solve this will unlock a new generation of AI capabilities that general-purpose models simply cannot deliver. The question is not whether Industrial Foundation Models will matter. It is which domains will see adoption first and who will build the data ecosystems to make them work. Where do you see Industrial Foundation Models making the biggest impact? I am curious to hear your perspective. Vlad Larichev | Florian Götz | Dr.-Ing. Tobias Guggenberger | Octavian Ciupitu | Wenhui Zhang, PhD #IndustrialAI #FoundationModels #DigitalEngineering

🚀 LLMs + ML = The Future of Intelligent Workflows 🤖💡 AI is evolving rapidly—and the fusion of Large Language Models (LLMs) with traditional Machine Learning (ML) is creating a new layer of sophistication in enterprise systems. 🔍 Why it matters: With over 58% of organizations already adopting LLMs, they’re no longer just a research novelty. LLMs are being embedded into ML pipelines to bring contextual intelligence, automation, and human-like reasoning to already-proven predictive systems. 🧠 What’s the difference? LLMs (like ChatGPT, Claude, Gemini) shine in natural language understanding, contextual reasoning, and handling unstructured text—powered by transformer architectures. Traditional ML dominates structured data, statistical modeling, and predictive analytics—offering speed, scale, and reliability in production. 🤝 Better Together: How LLMs Are Enhancing ML Pipelines ✅ Data prep automation – Reduce manual labeling by up to 80% ✅ Feature extraction – Generate rich features from unstructured data ✅ Contextual analysis – Add meaning to patterns in text, logs, and images ✅ Workflow optimization – Streamline preprocessing and modeling stages 🌍 What this enables: 🔗 Hybrid intelligence – Blend logic-driven ML with contextual AI 🏥 Domain-specific LLMs – Build vertical models for healthcare, legal, finance ⚙️ Enhanced automation – Context-aware business process acceleration ⚠️ But don’t forget: Integration brings challenges—latency, model complexity, and governance must be considered. The tech is powerful, but strategy must lead implementation. 🔵 LLMs won’t replace ML—they’ll amplify it. Together, they unlock the next level of enterprise AI. 💬 How is your organization leveraging LLMs to enhance existing ML workflows? #AI #MachineLearning #LLMs #EnterpriseAI #MLPipelines #AIAutomation #HybridIntelligence #DataScience #AIIntegration #NLP #AIinBusiness #FutureOfWork #IntelligentAutomation #DeepLearning #Transformers #AIEngineering #TechLeadership #AITrends2025 #ExplainableAI #DataStrategy

AI in real-world applications is often just a small black box; The infrastructure surrounding the AI black box is vast and complex. As a product builder, you will spend disproportionate amount of time dealing with architecture and engineering challenges. There is very little actual AI work in large scale AI applications. Leading a team of outstanding engineers who are building an LLM product used by multiple enterprise customers, here are some lessons learned: Architecture: Optimizing a complex architecture consisting of dozens of services where components are entangled, and boundaries are blurred is hard. Hire outstanding software engineers with solid CS fundamentals and train them on generative AI. The other way round has rarely works. UX Design: Even a perfect AI agent can look less than perfect due to a poorly designed UX. Not all use cases are created equal. Understand what the user journey will look like and what are the users trying to achieve. All applications do not need to look like ChatGPT. Cost Management: With a few cents per 1000 tokens, LLMs may seem deceptively cheap. A single user query may involve dozens of inference calls resulting in big cloud bills. Developing a solid understanding of LLM pricing and capabilities appropriate for your use case and the overall application architecture can help keep costs lower. Performance: Users are going to be impatient when using your LLM application. Choosing the right number and size of chunks, fine-tuned app architecture, combined with the appropriate model can help reduce inference latency. Semantic caching of responses and streaming endpoints can help create a 'perception' of low latency. Data Governance: Data is still the king. All the data problems from classic ML systems still hold. Not keeping the data secure and high quality can cause all sorts of problems. Ensure proper access and quality controls. Scrub PII well, and educate yourself on all applicable regulations. AI Governance: LLMs can hallucinate and prompts can be hijacked. This can be major challenge for an enterprise, especially in a regulated industry. Use guardrails are critical for any customer-facing applications. Prompt Engineering: Very frequently, you will find your LLMs providing answers that are incomplete, incorrect or downright offensive. Spend a lot of time on prompt engineering. Review prompts very often. This is one of the biggest ROI areas. User Feedback and Analytics: Users can tell you how they feel about the product through implicit (heatmaps and engagement) and explicit (upvotes, comments) feedback. Setup monitoring, logging, tracing and analytics right from the beginning. Building enterprise AI products is more product engineering and problem solving than it is AI. Hire for engineering and problem solving skills. This paper is a must-read for all AI/ML engineers building applications at scale. #technicaldebt #ai #ml