--- title: "Generative AI" description: "9 posts about Generative AI, summarised, each linking to the original." --- # Generative AI > 9 posts about Generative AI, summarised, each linking to the original. ## Articles ### [Becoming an AI Team](https://yomu.fyi/post/becoming-an-ai-team.md) - Company: [Pinterest](https://yomu.fyi/company/pinterest.md) - Author: Pinterest Engineering - Published: Sep 1, 2026 Becoming an AI team requires more than adding assistants to existing workflows: it changes ownership, planning, roles, and execution. At Pinterest, infrastructure teams face the scale of serving billions of Pins, boards, ads, and real-time signals, making AI adoption an operational necessity for reliability, cost efficiency, and developer productivity. AI code-generation and transformation models can turn a widespread legacy-codebase refactor, such as replacing “foo” with “bar,” from a months-long effort into work completed and verified in a week or less. As routine execution is increasingly augmented or automated, engineers, product managers, and designers are expected to spend more time on strategy, problem definition, and user validation, while managers emphasize vision, trade-offs, mentorship, and collective impact. The conclusion is that teams must continuously optimize an AI-driven operating system rather than treat AI as a side project. ### [How Discovery Bank delivers hyper-personalized banking at scale: behavioral AI, governed data, and real-time decisioning](https://yomu.fyi/post/how-discovery-bank-delivers-hyper-personalized-banking-at-scale-behavi.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Jack Yallop, Stuart Emslie - Published: Sep 1, 2026 Discovery Bank’s hyper-personalized banking model addresses how to make interactions relevant to individual behavior while meeting financial-services demands for scale, speed, security, and governance. The bank combines demographic, transactional, digital-engagement, savings, borrowing, credit-risk, rewards, and lifestyle-related data on the Databricks Data and AI Platform, producing governed reusable features, indicators, scores, forecasts, and recommendations. Behavioral models and a reusable next-best-action decisioning layer support personalization, fraud detection, servicing, and banker assistance; TRUST alerts assess transactions against client-specific norms and can escalate from explanation to account locking. A four-layer architecture adds control services and specialized generative AI and agents to trusted data and analytical foundations, with governance remaining in the loop. Reported results include a 40% uplift in client engagement impact, 20x faster pipeline development and data processing, 5x faster data-product creation, more than 300 models built per day, and return on investment above 500%. ### [How Deutsche Börse built a generative AI tool to tackle the large-scale migration of Zeppelin notebooks to Databricks](https://yomu.fyi/post/how-deutsche-bo-rse-built-a-generative-ai-tool-to-tackle-the-large-sca.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Evan Pandya, Tobi Wole-Fasanya - Published: May 19, 2026 Deutsche Börse Group is migrating a high volume of Zeppelin notebooks used by more than 2,000 users from Cloudera to Databricks, after Cloudera’s 2027 Zeppelin decommissioning and the group’s move to cloud analytics. Because the notebooks combine SQL, Python, custom interpreters, data-source references, visualizations, widgets, and scheduling logic, the team rejected fully rule-based rewriting. Its Zeppelin to Databricks Notebook Converter, delivered as a Databricks App, deterministically maps paragraphs to cells, translates interpreter syntax, reformats metadata as .ipynb JSON, and preserves original content. A generated, environment-specific prompt then gives Genie context about interpreters, data sources, and configuration patterns so it can reconstruct logic, ask clarifying questions, and handle the variable work. The hybrid workflow reduced redevelopment to 15–20 minutes per notebook, while keeping SQL, Python, visualizations, widgets, Oracle and HDFS references, scheduling, and custom business logic untouched; large-scale testing is next. ### [Machine Learning Use Cases: Practical Industry Applications](https://yomu.fyi/post/machine-learning-use-cases-practical-industry-applications.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Databricks Staff - Published: May 6, 2026 This guide surveys practical machine learning applications across industries, from medical imaging and fraud detection to demand forecasting, document processing, and customer-service automation. It defines supervised, unsupervised, semi-supervised, and reinforcement learning, then relates technique selection to the business question, data type, and availability of labels. Technical coverage includes convolutional neural networks for image analysis, transformers and large language models for generative AI, and time-series workflows using cleaning, lag features, and forecasting. The operational guidance addresses feature stores, experiment tracking, CI/CD, drift monitoring, retraining, cost optimization, fairness, privacy, explainability, and model risk management. Templates and checklists structure projects around business problems, data sources, metrics, architecture, measured outcomes, validation, governance, and escalation, while cited Databricks case studies and tools provide implementation examples. ### [AI success starts with clean data, not just better models](https://yomu.fyi/post/ai-success-starts-with-clean-data-not-just-better-models.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Aly McGue - Published: May 5, 2026 Kraken’s data transformation work argues that successful AI depends on clean, unified, accessible data rather than model quality alone. Serving more than 90 million customer accounts across 27 countries, the platform uses Databricks to distribute data securely and at scale, while clients need documentation, join logic and business context to make it useful. Unification reduces the analyst bottleneck, builds trust in shared numbers and enables self-service analytics, including conversational querying through Databricks Genie. The discussion also describes metadata as a live model input: Unity Catalog and Delta Sharing let Kraken share context alongside data instead of relegating it to PDFs or separate web pages. Reported client examples include call-center dashboards updated every few hours with predictive models and faster tariff experimentation, while organizations with stronger data skills and culture are positioned to adopt agentic AI more quickly. ### [LLM Vs AI: A Practical Guide to Differences, Use Cases, and Tools](https://yomu.fyi/post/llm-vs-ai-a-practical-guide-to-differences-use-cases-and-tools.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Databricks Staff - Published: May 1, 2026 This guide distinguishes artificial intelligence, a broad field covering systems that perform tasks associated with human intelligence, from large language models (LLMs), specialized generative AI models for language and code. It places LLMs within generative AI and deep learning, and explains how transformer self-attention processes tokens while training data, parameter scale, and context windows shape capability and limitations. Examples include content drafting, code generation, translation, customer-service chatbots, sentiment analysis, extraction, summarization, and tool-connected agentic workflows, while traditional machine learning remains suited to structured labels and numeric predictions. It recommends retrieval-augmented generation, human review, bias testing, privacy controls, and evidence-based pilots with defined workflows, metrics, budgets, realistic data, and logged outputs. ### [Generative AI for Business: A Complete Strategy and Implementation Guide](https://yomu.fyi/post/generative-ai-for-business-a-complete-strategy-and-implementation-guid.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Databricks Staff - Published: May 1, 2026 Generative AI is presented as an enterprise technology shift with potential value across customer operations, marketing and sales, software engineering, and research and development, which together are described as accounting for approximately 75% of use-case value across industries. The guide distinguishes generative systems, which create text, images, code, audio, or structured data from predictive models, and explains how foundation models and LLMs support open-ended business applications. Its implementation strategy prioritizes reliable data infrastructure, high-impact low-complexity pilots, and governance covering sensitive data, model limitations, monitoring, human review, and compliance. Recommended execution includes a cross-functional squad, predefined KPIs, user training, baseline measurements, and a 90-day executive review with scale, iterate, or discontinue decisions. Retrieval-augmented generation is identified as the most widely adopted way to reduce hallucinations by grounding responses in verified proprietary data, while ROI is expected within six to twelve months for a well-structured pilot. ### [AI Applications: Tools, Use Cases, and Platforms](https://yomu.fyi/post/ai-applications-tools-use-cases-and-platforms.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Databricks Staff - Published: May 1, 2026 The guide maps AI applications for data scientists, machine learning engineers, and technical leaders, covering predictive AI, generative AI, conversational AI, and autonomous agents across consumer, developer, and enterprise settings. It distinguishes consumer-facing tools from developer platforms and describes production concerns including model lifecycle management, vector search, data lineage, deployment, monitoring, governance, and evaluation. Generative systems create text, images, code, audio, and video from prompts, while large language models and mixture-of-experts architectures are presented as important foundations for enterprise applications; open models offer control over weights, governance, and deployment. The guide recommends defining use cases, assessing data readiness, and building privacy, bias-auditing, and monitoring controls before production, while noting that agents coordinate multi-step workflows across tools, APIs, and databases. ### [Grab AI Gateway: Connecting Grabbers to multiple GenAI providers](https://yomu.fyi/post/grab-ai-gateway-connecting-grabbers-to-multiple-genai-providers.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Bjorn Jee - Published: Feb 19, 2025 Grab AI Gateway is a central access layer connecting Grabbers’ applications to multiple GenAI providers, including OpenAI, Azure, AWS Bedrock, and Google VertexAI. Built as a set of reverse proxies, it centralizes provider authentication, authorization, rate limiting, auditing, and capacity management while presenting a unified OpenAI-style API; users can switch models by changing the model parameter. Exploration keys support short-lived staging experiments, while reviewed use cases can use longer-term service keys, and the gateway exposes embedding, image, audio, fine-tuning, search, and in-house open source models. Shared capacity, dynamic routing, monitoring, cost attribution, and usage records support governance and efficiency, although batch traffic can interfere with latency-sensitive services and provider-specific SDK edge cases require integration testing. More than 3,000 Grabbers have requested exploration keys, and the gateway powers applications including ride-safety audio analysis, content moderation, SQL generation, incident automation, and Slack support.