schema
Beyond Market Intelligence keeps schema in one place: 5 stories so far. The section currently leads with “Beyond Flat Tables A Practical Guide to Star Schema Dimensions”, “When Retrieval Fails, Loop Engineering Keeps Your Results on Track”, and “Schema proves process can match model power in ARC reasoning”. Dimensions often get treated as a single concept, but they come in distinct types, each serving a different analytical purpose. Most RAG pipelines return useful results most of the time. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every schema story on Beyond Market Intelligence, newest first.
Beyond Flat Tables A Practical Guide to Star Schema Dimensions
Dimensions often get treated as a single concept, but they come in distinct types, each serving a different analytical purpose. This guide breaks down those variations clearly, helping you choose the right structure for your data model. It's a practical read for anyone moving beyond basic spreadsheets and into more deliberate design. If you are curious about how structured thinking applies elsewhere, our piece on bridging retrieval and action offers a useful parallel.
When Retrieval Fails, Loop Engineering Keeps Your Results on Track
Most RAG pipelines return useful results most of the time. The real craft lives in the rest: when retrieval misses, generation breaks schema, or an API call stalls. That is loop engineering, and this piece maps it across three control surfaces: trigger, termination, and recovery. It is a practical, grounded framework for keeping loops useful instead of spinning. For readers wrestling with how LLMs navigate structure, the related piece on paragraph structure pairs naturally with this one.
Schema proves process can match model power in ARC reasoning
A 99% score on ARC‑AGI‑3 is the kind of number that makes you stop and ask how. Schema, a harness built around Claude Opus 4.8 and Fable 5, claims exactly that without touching model weights. It reshapes the process: turning observations into working game models, testing predictions against interaction history, and revising plans on the fly. The fallback rule is refreshingly simple, rerun below 80 with a different model and keep the higher score. That's practical engineering, not magic.
Stop parsing JSON manually and let AI handle structured data output
If you're tired of wrestling with JSON schemas and manual parsing, this guide is for you. It demonstrates a cleaner path: using Pydantic with OpenAI to get structured outputs directly from LLMs. The approach removes the fragile, time-consuming busywork, letting you trust the model's response instead of second-guessing every field. It's a practical shift toward more reliable data handling. For those ready to move beyond basic prompting, this is a solid next step.
Self-contained schemas make your data readable across past, present, and future tools.
Data formats usually die because they can't evolve. Seph Gentle thinks that's a solvable problem. His experimental format, Schemaboi, embeds self-contained schemas directly into file headers, so data stays readable without external definitions. It's a practical nod to HTML and HTTP's enduring adaptability. The focus on forward, backwards, and sideways compatibility means formats can shift without central coordination or data loss. That is a genuinely useful idea.