Articles 41–50 of 101
Tool calling explained: how an LLM asks your code to run actions like lookups, bookings and updates — and how to design tools that work reliably.
Embeddings explained without the maths: how text becomes numbers that capture meaning, what they power — search, recommendations, clustering — and their limits.
Vector databases explained: how they store embeddings and find similar meaning, when a dedicated one is worth it, and when Postgres is enough.
Retrieval-augmented generation explained in plain English: how RAG lets an LLM answer from your own documents, when to use it, and where it goes wrong.
How recruiters and HR teams use AI for scheduling, screening and candidate Q&A — and the bias, transparency and consent rules to design in from day one.
What CA and accounting firms can automate with AI: document collection, invoice and bank-statement extraction, reconciliation and client queries.
Where AI helps insurance agencies and brokers: renewal reminders, first-notice-of-loss intake, document collection and policy Q&A — with compliance guardrails.
How hotels, resorts and villas use AI to win direct bookings, answer guests 24/7 on WhatsApp, upsell stays and reduce OTA commission — with a realistic setup.
Proven AI for small and mid-size plants: visual quality inspection, predictive maintenance, demand planning and shop-floor knowledge assistants.
How law firms use AI safely: client intake, document review, research assistance and first drafts — with the confidentiality and verification rules that matter.
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