Prompt Engineering for AI: Advanced

Twenty-four pages for after the beginner tutorial. You learn how instruction roles and priorities work and how providers differ, how to design reasoning tasks and chain prompts into stages, how to request structured JSON and validate it with schemas and business rules, and how to turn prompts into versioned templates with variables.

Further pages cover multimodal and long-document prompts, sampling and reasoning settings, prompt caching and cost estimates, and then the part that makes prompting engineering: Python experiments with test cases, JSONL logs and metrics, evaluation datasets with held-out sets and baselines, robustness tests, red-teaming and regression checks. You finish with prompt security, privacy review, prompt operations and team handover.

All Python runs offline in this tutorial. Small toy providers written on the page stand in for a model so that your test harness can be checked repeatably; real models are less predictable, which is exactly why you test. Provider-specific details appear only in clearly marked notes with a date. A practice lab and a capstone, a tested prompt system for one business workflow, finish the tutorial. Every page is available in English and Bangla.