AI in Practice

LLMs for Business — what they can and can't do

A grounded view of where Large Language Models actually excel, where they fail, and the myths worth dispelling before committing any project.

Prompt Engineering — the practical framework

Structured prompting pays off. Role + task + context + constraints + format + examples = reliable outputs instead of dice-rolls.

RAG — Retrieval-Augmented Generation

Give an LLM access to your own documents at query time. The most effective pattern to get accurate, grounded answers on your data.

AI Agents — what they do and where they break

An agent is an LLM that plans, calls tools, and iterates until a goal is reached. Powerful for multi-step work but brittle — know when to trust one.

Document Automation — invoices, contracts, forms

Turn incoming PDFs / scans / emails into structured data automatically. One of the highest-ROI AI use cases in most companies.

Customer Support Automation

Deflect FAQs with RAG, triage tickets with classifiers, pre-draft responses for agents. Done right, it cuts resolution time 30-50% without killing customer satisfaction.

Workflow Automation with AI (n8n, Zapier, Make + LLM)

Combine no-code orchestrators with LLM nodes to automate business processes — data entry, email triage, report generation — without writing an app.

Prompting vs RAG vs Fine-tuning — how to choose

Which technique fits your problem? A decision framework by use case, with real-world trade-offs (cost, complexity, freshness, control).

AI ROI & Metrics — how to measure real value

Most AI projects fail on measurement, not tech. Here's what to track to know if it's actually working and to justify scaling the budget.

AI Risks — hallucinations, prompt injection, privacy

The three big classes of risk on production AI systems, and the practical mitigations that actually work.