Prompt Engineering: 7 Practical Guides for LLM Teams
One pillar on training-time practices, plus six focused guides on the techniques that decide whether a prompt survives contact with production traffic.
The six focused guides
Each guide stands alone and answers one question in depth. Read them in any order.
Few-Shot Prompting: A Practical Guide to In-Context Examples
How many examples to include, how to pick them, and why hard examples teach more than easy ones. A working method for building few-shot prompts that hold up under evaluation.
Read the guideForcing Structured Output: JSON, Schemas, and Validation
Stop parsing prose. Define the output contract, constrain decoding where you can, validate the result, and retry on failure with a repair prompt.
Read the guideHow to Evaluate LLM Prompts Without Fooling Yourself
Freeze a small case set, score it with a rubric you can defend, run it in CI, and stop shipping prompt changes on vibes. How to build an evaluation harness for prompts.
Read the guideNegative Prompting: What It Is and What Actually Works
Negative prompts work far better in image models than in text models. An honest look at where exclusions work, where they backfire, and what to use instead.
Read the guidePrompting AI Agents: Task Decomposition and Tool Use
Agents fail at planning, not at prose. How to write instructions that survive tool errors, ambiguous requests, and the urge to do five things at once.
Read the guideCutting Prompt Token Cost Without Losing Output Quality
Cache the stable prefix, compact the variable part, route to the right model, and measure what a token actually costs you. Where prompt engineering saves money and where it does not.
Read the guide