Prompt Engineering — How to Write Prompts That Work
Prompt Engineering — How to Write Prompts That Work
The difference between a mediocre AI response and a brilliant one almost always comes down to the prompt. Learn the six pillars that professionals use to get outstanding results from Claude every single time.
Why Prompt Engineering Matters
Prompt engineering is the skill of crafting inputs that consistently produce the outputs you want from an AI model. It is not about tricking Claude or using magic keywords — it is about communicating clearly. Think of it this way: if you walked into a room and said “write me something about marketing,” you would get a wildly different result than if you said “write a 500-word LinkedIn post targeting SaaS founders about the three biggest content marketing mistakes in 2025, using a conversational but authoritative tone, with a hook in the first sentence and a clear call-to-action at the end.” The same principle applies to AI. The quality of your output is directly proportional to the quality of your input.
Most people who are disappointed with AI results are not dealing with a limitation of the model — they are dealing with a limitation of their prompt. The good news? Prompt engineering is a learnable skill, and the six pillars below will transform your results immediately. These principles work with Claude, ChatGPT, Gemini, and any other large language model, but they are especially effective with Claude because of its strong instruction-following capabilities.
The 6 Pillars of Effective Prompting
Be Specific — Vague Inputs = Vague Outputs
The single most important rule in prompt engineering is specificity. Every ambiguous word in your prompt is an opportunity for the model to guess — and guess wrong. Instead of “write something about dogs,” say “write a 300-word informative paragraph about the top three health benefits of owning a dog, citing recent studies, aimed at first-time pet owners.” Specificity removes ambiguity. It tells Claude exactly what you want, how long it should be, who the audience is, and what evidence to include. The more specific your prompt, the fewer revisions you will need. This one pillar alone solves the majority of disappointing AI interactions.
Provide Context — Role, Audience, Industry
Context is what transforms a generic answer into a tailored one. Tell Claude who it should act as (“You are a senior financial analyst”), who the audience is (“This is for C-suite executives with no technical background”), and what industry or domain applies (“in the European fintech sector”). Context gives Claude a lens through which to filter its enormous knowledge base. Without context, Claude writes for a generic audience in a generic tone. With context, it writes for your audience in your tone. Think of context as the briefing you would give a new colleague before asking them to write something on your behalf.
Define Format — Bullet Points, Table, JSON, Code
Claude can produce output in virtually any format — but only if you ask. Want a comparison? Request a table. Need structured data? Ask for JSON. Want a quick overview? Request bullet points. Building software? Specify the programming language and framework. Format instructions prevent you from getting a 2,000-word essay when you needed a quick bullet list, or a wall of text when a neatly formatted table would have been perfect. Always specify your desired format explicitly: “Present this as a markdown table with columns for Feature, Pro, and Con” or “Return the result as a valid JSON array.”
Set Constraints — Word Count, Tone, Language
Constraints are guardrails that keep the output within your requirements. Common constraints include: word or character count (“maximum 200 words”), tone (“professional but approachable”, “casual and witty”), language (“respond in Czech”), reading level (“explain like I am 12”), and exclusions (“do not mention competitor products”, “avoid jargon”). Constraints are especially valuable in professional settings where your output needs to meet specific brand guidelines, compliance requirements, or publishing standards. Without constraints, Claude defaults to a helpful but generic style that may not match what you need.
Give Examples — Few-Shot Prompting
Few-shot prompting means including one or two examples of the desired output directly in your prompt. This is one of the most powerful techniques available because it shows Claude exactly what you want instead of just describing it. For example, if you want product descriptions in a specific style, include two sample descriptions and then say “Now write one for this product following the same style.” Claude is exceptionally good at pattern-matching from examples. Even a single example (one-shot) dramatically improves consistency. This technique is indispensable for repetitive tasks like generating catalog entries, formatting data, or writing responses in a branded voice.
Think Step by Step — Chain-of-Thought Reasoning
For complex problems involving math, logic, multi-step analysis, or decision-making, adding “think step by step” or “show your reasoning” to your prompt dramatically improves accuracy. This technique, called chain-of-thought (CoT) prompting, encourages Claude to break a hard problem into smaller pieces and solve each one sequentially rather than jumping to a final answer. It is especially useful for tasks like financial calculations, code debugging, strategic planning, and any scenario where the answer depends on multiple intermediate steps. Without CoT, Claude may skip steps and arrive at incorrect conclusions. With it, you can follow its reasoning and catch errors early.
Before and After: Prompt Comparison
The difference between a weak prompt and a strong prompt is dramatic. Here are two real-world examples that demonstrate the transformation:
Copyable Prompt Template
Use this template as a starting point for any prompt. Fill in the sections that apply to your task and delete the ones that do not. Over time, you will internalize this structure and write effective prompts naturally.
Task: [Specific action verb] + [what you need].
Context: The audience is [who]. The industry is [what].
Format: Return as [bullet points / table / JSON / code / essay].
Constraints: [word count] words. Tone: [tone]. Language: [lang].
Example: Here is an example of what I want: [paste example].
Reasoning: Think step by step before answering.
Common Prompt Mistakes
Even experienced users fall into these traps. Recognizing them will save you countless revision cycles and frustrating back-and-forth conversations with Claude.
- Prompt engineering is about clear communication, not magic keywords — quality in = quality out
- The 6 pillars: Be Specific, Provide Context, Define Format, Set Constraints, Give Examples, Think Step by Step
- Few-shot prompting (including 1–2 examples) is the most powerful technique for consistent results
- Chain-of-thought (“think step by step”) dramatically improves accuracy for complex reasoning tasks
- Use the prompt template as a starting point: Role, Task, Context, Format, Constraints, Example, Reasoning
- Avoid common mistakes: too vague, too long, conflicting instructions, and not iterating on results
Komplexní průvodce prompt engineeringem — šest pilířů, které přemění vágní interakce s AI na přesné a kvalitní výstupy pokaždé.
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