What is prompt engineering?

Prompt engineering is the practice of writing and refining instructions for language models so they produce useful, accurate, and well-formatted outputs.

What makes a good prompt?

A good prompt gives the model a clear role, specific instructions, context, examples when needed, and a defined output format. It also tells the model what to avoid.

What is few-shot prompting?

Few-shot prompting includes a few input-output examples in the prompt. It helps the model understand the pattern you want without fine-tuning.

How do I reduce hallucinations?

Give the model grounded context, ask it to cite sources, instruct it to say when it does not know, and keep the task scope narrow.

Prompt Engineering Basics: Write LLM Prompts That Work

Bottom line: Prompt engineering is not magic. It is clear communication. The better your instructions, the more predictable the output.

Start with a role and a task

Tell the model who it is and what you want. A vague request gets a vague answer. A specific request gets a useful one.

You are a technical editor. Summarize the following release notes in two sentences aimed at senior engineers.

Add context and constraints

Context helps the model understand the situation. Constraints keep the answer focused. Include background, audience, tone, length, and format.

Context: Our product is a B2B SaaS analytics platform.
Audience: Non-technical executives.
Tone: Professional but concise.
Length: Under 150 words.
Format: Bullet points.

Use few-shot examples

When the output format matters, show the model what good looks like. Two or three examples are usually enough to steer style and structure.

Input: The API latency increased from 50ms to 400ms.
Output: INCIDENT: API latency spike. IMPACT: 8x slower responses. ACTION: Investigate database connections.

Ask for structured output

Models are better at producing structured text when you describe the structure explicitly. Ask for JSON, Markdown tables, or XML tags.

Return JSON with these keys: summary, key_points, action_items. Do not include markdown code fences.

Reduce hallucinations

Provide relevant context in the prompt instead of relying on training memory.
Ask the model to cite the source of each claim.
Include an explicit instruction to say "I do not know" when the answer is not in the context.
Break complex tasks into smaller, verifiable steps.

Iterate like a product manager

Prompt engineering is iterative. Test one change at a time on a diverse set of inputs. Keep a prompt version history and track which version performs best on real examples.

Common mistakes

Asking for too many things in one prompt.
Using ambiguous words like "good" or "better" without defining them.
Skipping examples for tasks that require a specific format.
Trusting the first output without testing edge cases.

Published 2026-06-12

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Prompt Engineering

Glossary

Prompt engineering is the practice of crafting inputs to a language model so it produces better outputs without changing the model's weights. It covers word choice, structure, examples, constraints, and the order in which information appears. A well-engineered prompt can turn a mediocre response into a precise, actionable one. Effective prompts are usually clear, specific, and formatted. They state the task, define the audience, set the output format, and include any constraints. Adding examples, known as few-shot prompting, helps the model understand patterns that are hard to describe in words. Breaking complex tasks into steps, called chain-of-thought prompting, improves reasoning and arithmetic. Prompt engineering is iterative. You write a prompt, test it on diverse inputs, measure the results, and refine. Tools like the VePrompts Prompt Optimizer can surface issues such as ambiguity, missing constraints, or conflicting instructions. Good prompt engineering is often the fastest way to improve an AI feature before investing in fine-tuning or custom infrastructure.

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