LLMs

How to decide when a business workflow should avoid public LLM endpoints, based on data sensitivity, contractual exposure, and safer design alternatives.

A plain-English guide to the main layers of a modern AI system, from models and prompts to retrieval, tools, guardrails, and review.

How to use LLMs to turn messy receipts, descriptions, and invoices into structured expense categories without weakening accounting controls.

How to choose a hostable open-weight model based on task fit, hardware limits, governance needs, and support burden rather than hype.

What a private LLM deployment means in practice, when it makes sense, and how to compare managed private inference, self-hosting, and hybrid architectures.

How to design a spreadsheet assistant with safe permissions, table awareness, formula guardrails, and a realistic product scope for business users.

A practical comparison of large language models and classical machine learning, with guidance on when each approach fits a business problem.

A practical guide to writing prompts that produce useful, controlled outputs for real business work rather than clever toy demos.

TL;DR for operators A company analyzing thousands of support conversations needs categories that explain what customers are discussing without exposing their individual messages. Conventional systems commonly group numerical representations of messages, then generate descriptions for the resulting clusters. But geometric similarity does not necessarily produce the most informative topic labels. CALLIOPE1 offers an alternative: LLMs recursively summarize documents, synthesize categories from aggregated keywords, and assign records to those categories without embedding-based clustering. Its private version provides a formal document-level differential-privacy guarantee for released topic hierarchies. ...

TL;DR for operators Catalog teams often need more labeled examples before an attribute extractor works reliably in a new category or marketplace. Producing additional product-like text is not the difficult part. The training record has to change the intended attribute while leaving unrelated product information coherent. Negri, Martínez Gómez, Balanya, and Rajaram test a controlled generation pipeline built around that requirement.1 In their downstream experiment, synthetic-only training reached 60.48% attribute-extraction accuracy, almost identical to 60.79% for original-only training. But the best tested configuration was 75% original + 25% synthetic, at 68.82%. Accuracy declined as the synthetic share rose to 50% and 75%. ...