Practitioner's Guide
Beyond Market Intelligence keeps Practitioner's Guide in one place: 4 stories so far. The section currently leads with “Six architecture patterns to govern enterprise lakehouses with AI”, “Six Architectural Patterns for Scaling Reliable AI Agents”, and “Unlock Advanced RAG: 6 Architectures for Semantic Search & LLMs”. Chunking everything into a vector database and hoping for the best is not a governance strategy. Reactive loops are fine for demos, but production demands more. 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 Practitioner's Guide story on Beyond Market Intelligence, newest first.
Six architecture patterns to govern enterprise lakehouses with AI
Chunking everything into a vector database and hoping for the best is not a governance strategy. This guide from Towards Data Science lays out six concrete architecture patterns for turning an enterprise lakehouse into a governed AI platform. It moves beyond naive approaches to address real production needs. For readers tracking how AI exposes infrastructure fragility, the piece on "How AI is revealing the fragile foundations of the modern internet" offers a fitting companion perspective.
Six Architectural Patterns for Scaling Reliable AI Agents
Reactive loops are fine for demos, but production demands more. This guide walks through six architectural patterns that actually scale, moving beyond basic ReAct into autonomous, reliable systems built for real-world use. It's the kind of practical thinking that turns prototypes into products. For deeper context, our related piece on the hidden instability beneath LLM temperature zero explains why even deterministic settings can fracture after a hundred completions, a problem these architectures are designed to solve.
Unlock Advanced RAG: 6 Architectures for Semantic Search & LLMs
Basic graph retrieval only gets you so far. This guide walks through six production-oriented architectures that pair semantic search with knowledge graphs and LLM reasoning, giving practitioners a practical route beyond simple vector lookups. It's a grounded, no-nonsense read for teams ready to move past toy demos. For a deeper look at how LLMs handle structure internally, pair it with our piece on paragraph structure and token space. The ideas complement each other well.
See Through the Hype of Your AI Feature's Adoption Numbers
When a team opts into an AI feature, the lift you measure isn't just the tool's effect. It's a selection effect, a reflection of who chose to engage. This guide offers a practical framework for estimating what that opt-in feature actually did, even without randomization. It's a sharp, honest look at a messy problem. For practitioners ready to dig deeper into the mechanics of modern AI systems, our guide to distributed algorithms pairs well with this mindset.