RAG & Enterprise AI
Retrieval augmented generation, vector databases, embeddings, and building AI apps.
8 articles
Evaluating AI apps before you trust them: a practical playbook
Most teams adopt AI applications based on demos and vendor promises, then discover the gaps only after deploying them in production. This playbook gives you a structured sequence to test what actually matters before you commit budget, data, or workflows to any AI tool.
Aug 14, 2026Grounding AI in your company's knowledge: a practical playbook
Generic AI gives generic answers. This playbook shows you how to connect large language models to your organisation's own data so that every response is accurate, specific, and actually useful.
Aug 4, 2026RAG explained without the jargon: a practical playbook
Most LLMs confidently answer questions using knowledge that stopped updating months or years ago. RAG fixes that, and this playbook shows you exactly how to build it without getting lost in the technical weeds.
Jul 27, 2026Fine-tuning vs RAG: how to choose the right approach for your use case
Fine-tuning and retrieval-augmented generation solve different problems, and confusing the two leads to expensive mistakes. This article explains the mechanics of each approach and gives you a practical framework for choosing between them.
Jul 20, 2026Why most RAG deployments fail before they go live
Retrieval-augmented generation promised to make enterprise AI actually useful on proprietary data. The gap between that promise and production reality reveals a set of specific, fixable problems that most teams keep hitting in the same order.
Jul 13, 2026Why your RAG system keeps failing in production
Most enterprise RAG deployments look impressive in demos and disappoint in practice. The gap between a working prototype and a reliable production system is where the real engineering and strategic decisions happen.
Jul 6, 2026Why your RAG system keeps failing in production
Most enterprise RAG deployments look impressive in demos and underperform in real workflows. Understanding exactly where they break, and why, is what separates teams that get lasting value from those stuck in an endless pilot loop.
Jun 29, 2026RAG in the enterprise: why most deployments fail before they start
Retrieval-Augmented Generation promises to make your company's knowledge instantly accessible to AI, but the majority of enterprise deployments quietly underperform. The problem is rarely the AI model itself; it's everything that happens before the query reaches it.