Prompt Engineering
Designing instructions, examples, and context to get the best output from an LLM, the art and science of "talking to AI" effectively.
Quick answer
What is Prompt Engineering?
Designing instructions, examples, and context to get the best output from an LLM, the art and science of "talking to AI" effectively.
Last updated:
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.
Related terms
Browse all terms- Prompt Builder (Salesforce)Salesforce's no-code tool for designing, testing, and managing prompt templates that connect records to LLMs, core to the Einstein generative AI stack.
- LLM (Large Language Model)A neural network trained on massive text corpora to predict and generate text, the foundation behind ChatGPT, Claude, Gemini, and modern AI assistants.
See it in your Salesforce org
See Prompt Engineering running in GPTfy
Book 30 minutes with a GPTfy engineer to see how Prompt Engineering actually works inside a Salesforce org like yours.
Book a demo