TOPIC

Applied LLM patterns

Focused implementation patterns for using LLMs in products without confusing a model capability with a reliable system.

Articles in this topic

Write intent before code: an explore-plan-implement workflow for using natural language as a reliable interface to unfamiliar codebases.

One prompt turns a folder of meeting recordings, sales calls, and customer interviews into structured insights you can actually act on.

How to turn the books, talks, and blog posts of a domain expert you cannot hire into prompt rules your AI follows by default.

Learn how to convert images into detailed descriptions for visually impaired users and convert those descriptions into speech.

Automate document data entry with LLMs: extract fields, validate outputs, and reduce repetitive manual work without losing control.

Generate multilingual image descriptions with a vision model: a practical workflow for accessible content, translation, and safer structured outputs.

Build visual conversation apps with image-aware LLMs: a practical guide to multimodal inputs, prompts, and user-facing interaction.

When chain-of-thought prompting helps, when it fails, and how to use structured reasoning patterns without exposing private model reasoning.

Five techniques that beat turning up the temperature for getting diverse, coherent LLM outputs — with working Python for each.

Use examples in prompts deliberately: choose the right demonstrations, control edge cases, and measure whether examples improve model behavior.

Discover why using smaller, single-purpose prompts outperforms complex ones when building reliable LLM-powered applications.