Prompt Engineering Fundamentals: Write Instructions Models Follow
by Laura Mbeki
Get reliable output from language models, every single run.
Structured courses on writing prompts that hold up under load: role framing, constraints, few-shot design, evaluation and guardrails. You learn to measure prompt quality instead of guessing at it, and to build chains and agents that fail predictably. ChatGPT, Claude and open models are covered side by side so the technique survives a model change.
Showing 1–9 of 9 courses
Understand why models drift, then write instructions that hold across a long conversation.
Get valid JSON on the first attempt, and handle the cases where you still will not.
Fit the right material into a context window and cut token spend without losing accuracy.
Design tool schemas a model calls correctly, and handle the failures when it does not.
Build a small eval suite that tells you honestly whether a prompt change was an improvement.
Move from clever one-off prompts to versioned, tested prompt systems a team can maintain.
Ship model features that refuse the right things and never leak your system instructions.
Expose your own data and actions to assistants through MCP servers you write and control.
The full craft: prompt architecture, agent loops, evaluation harnesses and cost discipline.
Other shelves developers move on to next.