Prompt engineering isn’t done when the prompt ships. It’s done when the prompt survives production. In 2025, LLM-powered systems break silently. A prompt that worked yesterday can drift today—with zero code changes. If you’re not monitoring your prompts, you’re flying…
You can’t ship serious AI products without treating prompts like product logic. If you’re deploying LLM-powered features—chatbots, classifiers, summarizers—your prompts shouldn’t live in notebooks. They need to live behind robust, versioned, observable APIs. This guide walks through how to build…
Prompt engineering is no longer text-only. With GPT-4 Vision, Claude 3, and Gemini handling images, documents, charts—even audio—2025 demands a new discipline: multi-modal prompt evaluation. This post outlines how to evaluate image + text prompts systematically, measure performance, and build…
As prompt engineering matures, brute-force trial and error no longer cuts it. Complex tasks—multi-step reasoning, document synthesis, agent orchestration—need structured prompt refactoring. In this post, we explore reusable refactoring patterns to improve clarity, reliability, and output quality when basic prompting…
Few-shot prompts are powerful. But writing them ad hoc doesn’t scale. Serious AI teams treat few-shot prompts like code modules—reusable, versioned, tested, and optimized. This post walks through how to design, structure, and scale a few-shot prompt library that supports…
