Prompt Engineering
Definition
Prompt engineering is the practice of crafting inputs to LLMs that reliably produce desired outputs, including techniques like few-shot examples, structured formatting, and explicit instructions.
Why It Matters
The same LLM can be brilliant or useless depending on how you ask. Prompt engineering is the difference between “sometimes helpful” and “production-ready.” It’s not about tricks, it’s about communicating clearly with a system that takes instructions literally.
In production systems, prompt engineering directly affects reliability. A well-engineered prompt produces consistent outputs across varied inputs. A poorly engineered one works 80% of the time, creating debugging nightmares and user frustration.
For AI engineers, this is a core skill. You’ll spend significant time crafting, testing, and iterating on prompts. The patterns you learn transfer across models and use cases.
Implementation Basics
Fundamental Techniques
- Clear instructions: Be explicit about format, length, and constraints
- Examples (few-shot): Show the model what you want with 2-5 examples
- Structured output: Request JSON, XML, or markdown for parseable responses
- Role assignment: “You are a technical documentation writer…”
- Step-by-step: Ask the model to think through problems before answering
Prompt Structure A production prompt typically includes:
- System context (role, constraints, available information)
- Task description (what to do, what format)
- Examples (if few-shot)
- Current input
- Output format specification
Testing Prompts Never deploy a prompt without testing on diverse inputs. Create a test set covering edge cases, adversarial inputs, and common variations. Track metrics like success rate, format compliance, and output quality.
Common Mistakes
- Assuming the model understands implicit requirements
- Not specifying output format precisely
- Testing on one input and assuming it generalizes
- Ignoring model-specific differences (what works for GPT-4 may not for Claude)
- Over-engineering prompts before testing simple versions
Source
Chain-of-thought prompting, where examples include intermediate reasoning steps, significantly improves LLM performance on complex reasoning tasks.
https://arxiv.org/abs/2201.11903