Prompt Engineering
What prompt engineering is, how to think about crafting effective prompts, and how the Prompt Workbench supports the iteration process.
About
Prompt engineering is the practice of designing and refining the instructions you give a language model to get reliable, high-quality responses. Unlike traditional software where behavior is determined by code, a language model’s behavior is largely shaped by the prompt — the wording, structure, context, and examples you provide directly influence what the model produces.
In the Prompt Workbench, prompt engineering is a structured workflow: you write a prompt, test it against real inputs, evaluate the outputs, and iterate. The platform tracks every version, so you can measure whether a change improved results or regressed them, and roll back if needed.
Principles of a good prompt
Be explicit about the task. A model performs better when the instruction is unambiguous. Instead of “summarize this,” say “summarize this in three bullet points for a non-technical audience.” The more specific the instruction, the less the model has to infer.
Use the system message for behavior, the user message for input. The system message sets the model’s role, tone, and constraints. The user message carries the actual task or question. Keeping these separate makes it easier to reuse the same behavior across many different inputs.
Provide output format requirements. If you need JSON, a list, a specific length, or a particular structure, say so explicitly. Models follow formatting instructions well when they are clear and placed consistently in the prompt.
Use few-shot examples for complex tasks. When the task involves nuanced judgment or a specific style, including one or two assistant message examples shows the model exactly what you expect. Examples are more reliable than lengthy descriptions of what “good” looks like.
Keep context relevant. More context is not always better. Irrelevant context can distract the model and increase cost. Include only what the model needs to complete the task.
The iteration cycle
Prompt engineering is iterative. A first draft rarely performs optimally across all inputs — the process is:
- Write: Draft a prompt with a clear task, role, and output format.
- Test: Run it against a representative set of real inputs, not just the easy cases.
- Evaluate: Score the outputs — manually or with an automated evaluator — to identify where the prompt fails.
- Refine: Change one thing at a time. Adjust wording, add an example, tighten the instruction, or change the model.
- Compare: Use version history to compare the new version against the previous one on the same inputs.
Changing multiple things at once makes it hard to know what caused an improvement or regression. Small, targeted changes with consistent evaluation produce more reliable results.
Common failure modes
| Failure | Likely cause |
|---|---|
| Inconsistent output format | Format not explicitly specified, or specified only in prose |
| Model ignores part of the instruction | Instruction is buried, ambiguous, or contradicts itself |
| Output too long or too short | Max tokens not set, or length guidance missing from prompt |
| Model hallucinates facts | No grounding context provided, or no instruction to say “I don’t know” |
| Tone or style varies across runs | Persona or tone not defined in the system message |
Next steps
- Understanding Prompts: Prompt structure, roles, and model configuration.
- Prompt Variables: How to use variables to make a single template reusable.
- Versions and Labels: How versioning supports the iteration cycle.
- Create a Prompt from Scratch: Build your first prompt in the Workbench.