Implementing LLMs for Rapid Business Transformation

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?

Beyond the Hype: How Ontologies Can Unlock the Potential of Large Language Models for Business This post delves into the transformative capabilities of Large Language Models, such as GPT-4, and examines the crucial role that the structured intelligence of ontologies can play in deploying LLMs in production environments. 🔵 The Power of LLMs: LLMs have remarkable capabilities; they can craft letters, analyse data, orchestrate workflows, generate code, and much more. Companies such as Google, Apple, Amazon, Meta, and Microsoft are all investing heavily in this technology. Everything indicates that LLMs have enormous disruptive potential. However, there is a problem: they can hallucinate. So, how can serious businesses ever use their full power in production? 🔵 The Structure of Ontologies: Ontologies provide a formal and structured way to represent knowledge within specific domains. They enable computers to understand and reason in a logical, consistent, and controlled manner. Ontologies are also human-readable, editable, and auditable artefacts that can be kept under source control. 🔵 It's All 'Just' Semantics: The combination of LLMs and ontologies creates a powerful synergy. This synergy allows organisations to harness the capabilities of LLMs within the guardrails of a controlled structure. This collaborative partnership establishes a reinforcing feedback loop of continuous improvement. Ontologies provide context to prompts and validate the LLMs' responses, while the LLMs help extend ontologies with missing concepts. 🔵 Bringing It All Together in a Working Memory Graph: LLMs and Ontologies can be combined in a design pattern that I call the 'Working Memory Graph'. Within a Working Memory Graph (WMG), LLM embedding vectors, ontologies, facts from the Knowledge Graph, and graph-based analytics are all integrated. The WMG uses ontologies to help build prompts, translates natural language questions into a graph structure, employs Graph Retrieval-Augmented Generation (GRAG) to gather facts, and uses LLM-based GML to perform graph-based analytics. In summary, Large Language Models represent not merely a passing fad but a paradigm shift that progressive organisations can ill afford to ignore. Ontologies offer a versatile yet disciplined framework, enabling organisations to move forward with LLMs while remaining in control. ⭕ Working Memory Graph: https://lnkd.in/eQF4PE27 ⭕ Continuous and Discrete: https://lnkd.in/ex8HA_Nj ⭕ Vectorizing Your Knowledge: https://lnkd.in/eDDd3MAz ⭕ LLM-based Node Classification: https://lnkd.in/e_YzTg_V

RIP Tableau Tableau is a business intelligence tool owned by Salesforce. For years it was part of how we worked at Voi. In the beginning it felt powerful, but over time it turned into what many legacy SaaS tools become: expensive, clunky and slow. Every ad hoc request ended up in an analyst backlog. Local teams across our 100 plus cities were left waiting for insights, costs kept going up and speed disappeared. So we ripped it out, saved at least 500k EUR, potentially millions (from speed). The direct savings are hundreds of thousands of euros in licenses. The indirect savings are even bigger since analysts can now focus on high impact work instead of repetitive reporting. The biggest shift is speed. What once took weeks now happens in seconds. Here is how we made it possible: 1. We fixed the foundations. Years of work on data governance. Every metric has an owner, quality checks, semantics and definitions. Everyone in the company knows what a number means. With that in place, self serve became possible, which is essential when local teams in 100 plus cities need the right data at the right time. 2. We defined what we need, not what we paid for. A single source of truth, real time data streaming and self serve for non technical users. Analysts no longer spend their days on small one off requests. 3. We used LLMs as the bridge. Together with a design partner we built a UI that supports continuous business intelligence, and we created an AI data analyst that lives inside Slack and Sheets. LLMs translate natural language into SQL, query the warehouse and return insights or visuals in natural language again. This step is what unlocked true self serve at scale. But LLMs alone are not enough. In an enterprise setting you need strict guidelines and guardrails. Without governance you risk inconsistent answers, wrong definitions or even compliance issues. The combination of solid data governance with the power of LLMs is what makes this work. The results are clear: 1. Millions saved on SaaS and labor 2. One source of truth for all key metrics 3. Self serve for everyone in the company within clear constraints 4. Up to 100x faster time to insight and decision making LLMs made this shift possible. Strong governance made it safe. RIP Tableau. And it will not be the last legacy SaaS tool we replace.

Here is what we will realize when the dust settles on LLMs 💡 As we navigate the twists and turns of the Gartner hype cycle, edging closer to the valley of realization, it's becoming increasingly clear what the future holds for Large Language Models (LLMs) in the business world. Over the past two months, I've been on a journey, conversing with 60+ experts immersed in the realm of data. Our discussions have illuminated a truth that many of us perhaps knew but were unwilling to confront: When it comes to harnessing the power of LLMs to answer questions based on your company’s data, data isn't just king, it's the entire kingdom. Sure, LLMs can be a fantastic interface, but they're not a panacea. We've been expecting them to magically provide answers, but without the right data foundation, the magic wanes. An LLM is not very different to the rest of ML models when dealing with problems like garbage-in-garbage-out, and is only as good as the data it's built on and the rules that govern its usage. However, often our current state of data is akin to a castle built on sand. Poor quality, undefined access rights, and an unshared, disjointed business ontology make it impossible for LLMs to provide the insights we so desperately seek. So, what's the solution? It's high time we roll up our sleeves and start the crucial work: Improving Data Governance: Establish clear protocols and processes to manage your data efficiently and have a single source of truth for data access. Enforcing Data Quality & Integrity: Implement means of defining and enforcing data quality and integrity from the source; definitely look at data contracts and the work of Chad Sanderson Mapping Data into a Shared Business Ontology: Define a shared business ontology and map your data into it. Check out Tony Seale for some brilliant learnings from UBS. Distributing Ownership Responsibility: Distribute the ownership of data into a federated model. Enable domain teams governing access, classification, and protection rules of their data, while adhering to the company’s global data protection policies. The journey toward LLM readiness might seem daunting, but remember, every giant leap begins with a single step. Start by gaining an overview of your current data landscape. Assess the areas holding you back and identify business cases where improvements can yield measurable impact. Once you know where you stand, take that first step, and start making strides toward a data-driven future. Let’s discuss in the comments about the concrete steps you are taking to become LLM and AI-ready. #datagovernance #llm #AI

Just finished reading an amazing book: AI Engineering by Chip Huyen. Here’s the quickest (and most agile) way to build LLM products: 1. Define your product goals Pick a small, very clear problem to solve (unless you're building a general chatbot). Identify use case and business objectives. Clarify user needs and domain requirements. 2. Select the foundation model Don’t waste time training your own at the start. Evaluate models for domain relevance, task capability, cost, and privacy. Decide on open source vs. proprietary options. 3. Gather and filter data Collect high-quality, relevant data. Remove bias, toxic content, and irrelevant domains. 4. Evaluate baseline model performance Use key metrics: cross-entropy, perplexity, accuracy, semantic similarity. Set up evaluation benchmarks and rubrics. 5. Adapt the model for your task Start with prompt engineering (quick, cost-effective, doesn’t change model weights): craft detailed instructions, provide examples, and specify output formats. Use RAG if your application needs strong grounding and frequently updated factual data: integrate external data sources for richer context. Prompt-tuning isn’t a bad idea either. Still getting hallucinations? Try “abstention”—having the model say “I don’t know” instead of guessing. 6. Fine-tune (only if you have a strong case for it) Train on domain/task-specific data for better performance. Use model distillation for cost-efficient deployment. 7. Implement safety and robustness Protect against prompt injection, jailbreaks, and extraction attacks. Add safety guardrails and monitor for security risks. 8. Build memory and context systems Design short-term and long-term memory (context windows, external databases). Enable continuity across user sessions. 9. Monitor and maintain Continuously track model performance, drift, evaluation metrics, business impact, token usage, etc. Update the model, prompts, and data based on user feedback and changing requirements. Observability is key! 10. Test, Test, Test! Use LLM judges, human-in-the-loop strategies; iterate in small cycles. A/B test in small iterations: see what breaks, patch, and move on. A simple GUI or CLI wrapper is just fine for your MVP. Keep scope under control—LLM products can be tempting to expand, but restraint is crucial! Fastest way: Build an LLM optimized for a single use case first. Once that works, adding new use cases becomes much easier. https://lnkd.in/ghuHNP7t Summary video here -> https://lnkd.in/g6fPsqUR Chip Huyen, #AiEngineering #LLM #GenAI #Oreilly #ContinuousLEarning #ProductManagersinAI

LLMs Are Getting Better and Cheaper: Here’s How to Take Advantage Large Language Models (LLMs) are not just improving—they’re becoming more accessible and cost-effective for businesses of all sizes. But how can your company tap into this trend and integrate LLMs into your products, services, and operations? Let’s explore the challenges and the actionable steps you can take right now. The Challenges Holding Companies Back: 1– Technical Complexity Implementing LLMs internally requires new technologies and skills. The infrastructure setup can be daunting, and many firms find it challenging to get up and running. Example: A mid-sized company wants to use LLMs for customer support but lacks the in-house expertise to integrate the technology, causing delays and increased costs. 2– Data Quality LLMs thrive on high-quality, well-governed data. If your internal data isn’t up to par, it limits the overall impact of your LLM services. Example: An organization feeds unstructured and inconsistent data into an LLM, resulting in inaccurate insights that misguide business decisions. 3– AI Safety Issues like hallucinations—where the AI generates incorrect or nonsensical information—are still significant challenges. Managing AI toxicity and bias is crucial to maintain trust and compliance. Example: A chatbot unintentionally provides offensive responses due to biased training data, leading to customer complaints and reputational damage. Despite these challenges, the good news is that we’re still relatively early in the LLM adoption curve. Companies that start building now can gain a significant competitive advantage. Here are some suggestions to help you get started: 1– Integrate LLMs into Your Core Business Services Don’t hesitate to start small. Initiate hackathons or develop prototypes to explore how LLMs can enhance your core offerings. Hands-on experience is the best way to identify challenges and opportunities unique to your firm. 2– Partner with LLM-Powered Vendors for Non-Core Functions Collaborate with vendors specializing in LLMs for areas like accounting, finance, customer service, and customer experience. This approach offers quick ROI on your AI investment and provides inspiration for leveraging LLMs within your core services. 3– Empower Your Employees with AI Assistants Boost your team’s productivity by providing guidance and tools to leverage AI assistants effectively. Equipping your employees with AI skills enhances efficiency and fosters an innovative workplace culture. The evolution of LLMs presents a transformative opportunity for businesses willing to embrace it. By addressing the challenges head-on and taking proactive steps, your company can stay ahead of the curve. Want to Learn More? Check out my latest post “The Rise of GenAI and LLMs: Is Your Business Ready?” on the Ushur blog. (Link in comments)

As we move into a new era of #GenAI, implementing #LLMs in production presents many practical challenges. Over the past year, I have worked closely with several founders to explore these challenges and understand their concerns. Common issues include the deterministic vs. non-deterministic nature of these models, cost & latency problems, performance promises, and reliability in a production environment. To tackle these challenges and help other companies navigate them successfully, I decided to put together technical case studies. These studies explore how some companies have already overcome these obstacles, such as Postman's implementation of Postbot, which I had the opportunity to explore during a session hosted by Portkey. Alongside SaaSBoomi, I aim to address common problems and highlight the ways in which companies like Postman are leveraging technology to overcome these challenges. The goal is to provide actionable insights for founders to immediately implement some of these in their own companies. Grateful for the support of Rajaswa Patil, Rohit Agarwal, and Vrushank Vyas in helping me put this piece together. Going forward, the plan is to release one technical case study, every month. Have you faced any challenges working with LLMs? Were you able to overcome those challenges successfully? What steps did you take to solve those issues? I would love to hear your thoughts. https://lnkd.in/gATQFD5e #SaaS

The bottleneck isn't GPUs or architecture. It's your dataset. Three ways to customize an LLM: 1. Fine-tuning: Teaches behavior. 1K-10K examples. Shows how to respond. Cheapest option. 2. Continued pretraining: Adds knowledge. Large unlabeled corpus. Extends what model knows. Medium cost. 3. Training from scratch: Full control. Trillions of tokens. Only for national AI projects. Rarely necessary. Most companies only need fine-tuning. How to collect quality data: For fine-tuning, start small. Support tickets with PII removed. Internal Q&A logs. Public instruction datasets. For continued pretraining, go big. Domain archives. Technical standards. Mix 70% domain, 30% general text. The 5-step data pipeline: 1. Normalize. Convert everything to UTF-8 plain text. Remove markup and headers. 2. Filter. Drop short fragments. Remove repeated templates. Redact PII. 3. Deduplicate. Hash for identical content. Find near-duplicates. Do before splitting datasets. 4. Tag with metadata. Language, domain, source. Makes dataset searchable. 5. Validate quality. Check perplexity. Track metrics. Run small pilot first. When your dataset is ready: All sources documented. PII removed. Stats match targets. Splits balanced. Pilot converges cleanly. If any fail, fix data first. What good data does: Models converge faster. Hallucinate less. Cost less to serve. The reality: Building LLMs is a data problem. Not a training problem. Most teams spend 80% of time on data. That's the actual work. Your data is your differentiator. Not your model architecture. Found this helpful? Follow Arturo Ferreira.