Prompt engineering has an unfortunate name. It sounds like a dark art, but in an enterprise context it is closer to writing a very good work instruction: telling a capable but literal-minded colleague exactly what the job is, what good output looks like, and what to do when things get weird. The prompts that fail in production usually fail for mundane reasons — vague instructions, no examples, no plan for bad input — not because they lacked magic words.
The system prompt is your policy document
Every Claude request can carry a system prompt: standing instructions that frame the whole conversation, separate from whatever the user types. In business applications, this is where the important decisions live. A good system prompt covers four things: the role ("You are a claims-intake assistant for an insurance company"), the task and output format ("Summarize each claim in the JSON structure below"), the boundaries ("If the message asks about anything other than a claim, respond that you can only help with claims"), and tone ("Professional, plain English, no speculation").
Treat this text like configuration, not like a chat message. Put it in version control, review changes the way you review code changes, and never let individual developers tweak production prompts ad hoc. A one-word edit to a system prompt can change behavior across every request your application makes.
Examples beat adjectives
The single highest-leverage technique in business prompting is showing, not describing. Instead of writing "keep summaries concise and professional," include two or three worked examples of an input and the exact output you want. Models follow demonstrated patterns far more reliably than they follow abstract style guidance. This is often called few-shot prompting, and it is especially effective for format-sensitive tasks: extraction, classification, structured summaries.
Choose examples that cover your real distribution, including an awkward case — a document with missing fields, a message in the wrong language, an ambiguous request. Showing the model what to do when input is imperfect prevents a large class of production surprises.
Design for hostile and messy input
Demo prompts assume cooperative users. Production prompts meet copy-pasted email chains, empty messages, off-topic questions, and occasionally deliberate attempts to make the assistant misbehave. Your prompt should state explicitly what to do in each case: what to say when the request is out of scope, how to respond when required information is missing, and that instructions embedded in user-supplied documents are content to be analyzed, not commands to be followed. That last point is the heart of defending against prompt injection, which deserves its own reading — see Prompt Injection 101.
Also decide where user content ends and your instructions begin. Keep untrusted content clearly delimited — for example, inside marked sections of the prompt — so the model can tell your policy apart from a customer's document.
Iterate with measurement, not taste
Prompt changes feel subjective, which is why teams argue about them. The fix is to stop evaluating prompts by reading a few outputs and instead maintain a small evaluation set: a few dozen representative inputs with known-good outputs or scoring criteria. Every prompt change runs against the set before it ships. This turns "I think version B reads better" into "version B improved extraction accuracy and shortened outputs" — a conversation engineering managers can actually referee. The companion article on evaluating LLM features covers how to build this.
Two practical habits round this out. First, keep prompts stable per model version: when you upgrade models, rerun your evaluation set, because a prompt tuned for one version may behave differently on the next. Second, resist prompt sprawl. One well-maintained prompt per task beats five near-duplicates owned by nobody.
Where to go next
Prompting and testing are two halves of the same discipline — continue with Evaluating LLM Features: Test Before You Trust, or see structured outputs for getting parseable JSON reliably. For the basics of getting connected, start at the quickstart.