GPTfy Glossary
Prompt Engineering
Designing instructions, examples, and context to get the best output from an LLM — the art and science of "talking to AI" effectively.
Prompt engineering treats prompts as a programming surface. A well-engineered prompt typically includes: (1) a role definition ("You are a senior Salesforce admin..."), (2) the task description, (3) input data and constraints, (4) examples (few-shot), (5) the desired output format, and (6) edge-case instructions.
Effective patterns include: chain-of-thought (asking the model to reason step by step), few-shot (showing examples of the desired pattern), and structured outputs (asking for JSON or specific schemas). Anti-patterns include vague instructions, missing context, and not specifying format.
For Salesforce, prompt engineering happens at multiple layers: admins in Prompt Builder, developers in Apex/Flow, and platform vendors like gptfy who write the underlying templates that ship with their features. Prompt versioning, A/B testing, and observability are emerging as production disciplines.
See Prompt Engineering in GPTfy
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