Beyond Chatbots: How LLMs Are Rewiring Business Automation
Large language models have moved from answering questions to making decisions inside automated workflows. This article examines what is changing in business automation and why.
Structured Outputs Are the Real Unlock for LLM Applications
The distinction between an unreliable AI demonstration and a dependable system is rarely the model itself, but whether it is required to return structured data that programs can act upon.
Designing Human-in-the-Loop Approval Steps
A practical guide to inserting human approval into automated and AI-driven workflows: where to place gates, how to pause execution, and how to fail safely.
Connecting AI to Tools: An Introduction to Function Calling
An engineer's introduction to function calling: how language models invoke external tools through structured schemas, and how to design the loop safely.
When Not to Use an LLM
A practical decision framework for backend engineers on when a large language model is the wrong tool, and which deterministic alternatives to reach for instead.
AI Agents vs. Workflows: Choosing the Right Tool
A decision framework for choosing between deterministic workflows and autonomous AI agents, weighing predictability, cost, and the nature of the task.
RAG Explained: Giving LLMs Access to Your Own Data
An overview of retrieval-augmented generation: how it grounds language models in your own documents, when to use it, and the components of a working pipeline.
Prompt Engineering Fundamentals for Reliable Automations
A practical guide to writing prompts that produce consistent, machine-parseable output for production automations, covering structure, examples, and constraints.