--- title: "AI" description: "65 posts about AI, summarised, each linking to the original." --- # AI > 65 posts about AI, summarised, each linking to the original. ## Articles ### [Forward Deployed Engineering](https://yomu.fyi/post/forward-deployed-engineering.md) - Company: [Ramp](https://yomu.fyi/company/ramp.md) - Author: Leo Mehr - Published: Aug 5, 2025 Forward Deployed Engineering (FDE) is presented as a customer-facing engineering function for B2B companies that need to integrate software deeply, customize implementations, and deliver outcomes for enterprise customers. Palantir is described as creating the role after its founding in 2003, with engineers deployed at military sites and customer offices; the model later spread to companies including Scale AI, C3.ai, Databricks, and Snowflake. At Ramp, the team grew from two to 16 FDEs after emerging in fall 2023, moving from firefighting toward generalized features, platforms, and AI that support enterprise product-market fit and new verticals. Its operating model spans prospects, implementation, rollout, and long-tail support, while “always be scoping” encourages direct requirement questioning, workarounds, and generalized solutions. The hiring guidance prioritizes drive, ownership, customer empathy, and communication alongside sufficient technical fundamentals, arguing that judgment about quick fixes versus scalable solutions is central to the role. ### [From failure to success: The birth of GrabGPT, Grab’s internal ChatGPT](https://yomu.fyi/post/from-failure-to-success-the-birth-of-grabgpt-grab-s-internal-chatgpt.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Wenbo Wei - Published: May 19, 2025 In March 2023, an effort to reduce repetitive questions overwhelming Grab’s ML Platform support channels began as a chatbot for platform documentation and became GrabGPT, an internal ChatGPT-like tool. The initial chatbot used chatbot-ui and more than 20,000 words of Q&A documentation, but GPT-3.5-turbo’s 8,000-token limit forced summaries below 800 words; embedding search also performed poorly, so the approach was abandoned. The pivot extended existing frameworks, added Google login, and deployed a tool through Grab’s catwalk model-serving platform, later named GrabGPT. It reached 300 registered users on day one, more than 3,000 users and 600 daily active users by month three, and nearly all Grabbers according to the post, with private routing, global accessibility, multi-provider model support, and auditable interactions cited as reasons for adoption. ### [Supercharging LLM application development with LLM-Kit](https://yomu.fyi/post/supercharging-llm-application-development-with-llm-kit.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Boon Zhan Chew - Published: Nov 29, 2024 Grab developed LLM-Kit, a comprehensive framework for setting up production-ready Generative AI applications while addressing security, scalability, integration, monitoring, and standardisation concerns. Its pre-configured structure includes an API server, configuration management, a sample LLM Agent, tests, and an integrated stack spanning Poetry, Gunicorn, FastAPI, LangChain, LangSmith, HashiCorp Vault, Amazon EKS, and GitLab CI. Engineers submit application details through a form, which triggers GitLab project and code-scaffold generation, CI/CD setup, Terraform-based staging and production infrastructure provisioning, and secure GPT-token storage in Vault. The scaffold also includes authentication, API documentation, observability, Redis and vector-database integrations, deployment pipelines, and a cookbook with organisational examples; the post reports that it has onboarded hundreds of GenAI applications and saves an estimated 1.5 weeks before teams begin their first feature. ### [What is a Vector Database?](https://yomu.fyi/post/what-is-a-vector-database.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Databricks Staff - Published: Oct 11, 2024 Vector databases store and manage high-dimensional vector representations of data, grouping them by similarity rather than traditional row-and-column organization. They support semantic and similarity-based retrieval by converting text, images, audio, or video into embeddings, indexing them with methods such as product quantization and HNSW, and comparing query vectors with indexed vectors. These capabilities underpin use cases including RAG, hybrid search, recommendations, NLP, image and video recognition, and anomaly detection, while sharding, partitioning, caching, and replication support scale. The article distinguishes a vector database from a standalone index because it also provides storage, CRUD, metadata filtering, horizontal scaling, and data management functions, and describes Databricks AI Search as an integrated implementation with automatic scaling, optimization, security, and governance. ### [How Ramp builds customer-first AI](https://yomu.fyi/post/how-ramp-builds-customer-first-ai.md) - Company: [Ramp](https://yomu.fyi/company/ramp.md) - Author: Yunyu Lin - Published: Jul 11, 2023 Ramp describes principles for building customer-focused AI, contrasting embedded outcomes with superficial chatbot experiences it calls “AI washing.” The approach emphasizes integrating models into workflows, separating general models trained on aggregated and masked data from sensitive models that use private customer data temporarily through in-context learning, and requiring explicit opt-in for sharing contract data. It prioritizes user control and feedback over lengthy explanations, while using guardrails that constrain outputs through predefined interactive data blocks rather than relying only on censorship filters. Ramp cites contract term extraction, spend intelligence, pricing benchmarks, Copilot, and Jsonformer as examples of these practices. The stated goal is AI that saves customers time and money while protecting privacy and producing safer, more useful results. [Newer posts](https://yomu.fyi/topic/ai/page/3.md)