Prompt Engineering Basics

What Is Prompt Engineering?

When you interact with an LLM, your prompt is the only thing guiding its response. Unlike traditional software where you click buttons or fill forms, with LLMs your instructions are written in natural language — and how you write them dramatically affects what you get back.

Prompt engineering is the practice of crafting inputs that consistently produce useful, accurate, and well-structured outputs. It’s part writing skill, part understanding of how LLMs process instructions. It’s the most immediately practical AI skill you can develop, because it works with every model, requires no coding, and produces results right away.

Core Techniques

Zero-Shot Prompting

The simplest approach: just ask. No examples, no special formatting — a direct question or instruction.

“Summarize the key points of this article.”

Zero-shot works well for straightforward tasks where the model already understands what you want. It’s quick but gives you less control over the output format and style.

Few-Shot Prompting

Provide a few examples of what you want before the actual task. The model picks up the pattern and follows it.

Show 2-3 examples of the input/output format you want, then provide the real input. The model infers the pattern from your examples — it’s learning from context, not from additional training.

Few-shot is powerful when you need a specific output format, tone, or approach that’s hard to describe in words. Showing is often more effective than telling.

Chain-of-Thought

Ask the model to think step by step before giving a final answer. This simple addition significantly improves performance on reasoning tasks — math, logic, analysis, debugging.

Adding “Think through this step by step” or “Show your reasoning” encourages the model to work through intermediate steps rather than jumping to a conclusion. Since LLMs generate tokens sequentially, the reasoning tokens influence what comes after, leading to more accurate final answers.

System Prompts

Most LLM interfaces support a system message that sets the model’s role, personality, and constraints before the conversation begins. This is where you define who the model is and how it should behave.

A good system prompt might say: “You are a senior software engineer. Give concise, practical answers. Use code examples when relevant. If unsure, say so.”

System prompts are persistent — they influence every response in the conversation, not just the first one.

Anatomy of a Good Prompt

The most effective prompts tend to include these elements:

Role — Who the model should be. “You are an experienced data analyst” gives context that shapes the response style and expertise level.

Task — What you want done. Be specific. “Analyze this data” is vague. “Identify the top 3 trends in this quarterly sales data and explain their likely causes” gives clear direction.

Constraints — Boundaries and requirements. “Keep your response under 200 words.” “Use bullet points.” “Don’t include technical jargon.” Constraints prevent the model from going off-track.

Format — How you want the output structured. “Respond in JSON.” “Use a numbered list.” “Include a summary section at the end.” Explicit formatting instructions save you from reformatting the output yourself.

Key Terminology

System message — Persistent instructions that define the model’s behavior for an entire conversation. Set before user messages.
Temperature — A setting that controls randomness. Low temperature (0-0.3) gives more predictable, focused responses. High temperature (0.7-1.0) gives more creative, varied responses.
Context window — The total amount of text (prompt + response) the model can work with at once. Long prompts leave less room for the response.
Hallucination — When the model generates confident-sounding but incorrect information. Good prompts can reduce (but not eliminate) this.

Why Does It Matter?

Prompt engineering is the difference between getting mediocre results and getting excellent results from the exact same model. Two people using the same LLM can have vastly different experiences based solely on how they write their prompts.

It matters practically because most people interact with AI through prompts, not through fine-tuning or building custom models. Getting better at prompting has the highest return on investment of any AI skill — it costs nothing, applies everywhere, and improves immediately with practice.

Common Misconceptions

“There’s one perfect prompt for every task.” Prompting is iterative. Your first attempt is rarely your best. Good prompt engineers refine and adjust based on what works. Different models also respond differently to the same prompt.

“Longer prompts are better.” More context can help, but unnecessary length wastes tokens and can actually confuse the model. Be as specific as needed, but no more. Clarity beats length.

“Prompt engineering will become obsolete.” As models improve, they handle vague instructions better. But the core skill — communicating clearly what you want — remains valuable. Models that understand you better still produce better results with better prompts.