Prompt engineering for software teams
Prompt engineering for software teams is the discipline of writing prompts that consistently produce useful output from LLMs in engineering workflows, code generation, test authoring, PR review, technical writing.
The discipline has converged on a small set of practices: provide context up front (project conventions, file structure, the surrounding code); state output format explicitly (JSON schema, file-level edits, specific commit-message format); include negative examples (what NOT to do); use few-shot examples when the task is unusual; constrain output with structured schemas where possible. Beyond single prompts, the modern model is agentic. The LLM is given tools (read file, write file, run tests, query database) and a multi-step task. The shift from 'design a prompt' to 'design an agent loop with feedback' is happening across most production engineering AI applications. Stride's research finds prompt design accounts for ~50% of the variance in deployed AI productivity gains.