A plain-language guide for performance marketers
Definition
Prompt engineering is the practice of designing and refining the instructions given to an AI model to produce reliable, accurate, and useful outputs. A well-engineered prompt specifies the role the AI should take, the task it needs to complete, the format of the desired output, and any constraints or examples that guide the response. For performance marketers, prompt engineering determines the quality of everything from ad copy generated by ChatGPT to Google Ads audit reports produced by Claude Code. The difference between a vague prompt ("analyse my campaign") and an engineered prompt ("you are a senior Google Ads specialist with 10 years of experience. Analyse the attached search terms report and identify the top 5 wasted spend patterns, ranked by estimated monthly waste. Present findings as a table with keyword, match type, monthly spend, and recommended action") is the difference between a generic response and an actionable report.
These two terms are often confused. They describe related but distinct things. Prompt engineering is the instruction itself. Context engineering is the full environment you build around that instruction.
| Dimension | Prompt engineering | Context engineering |
|---|---|---|
| What it covers | How to word a single instruction | Everything the AI receives in the session |
| Includes | Role, task, format, constraints | Prompt + examples + documents + history + memory |
| Scope | One request at a time | Entire AI session or workflow |
| Primary skill | Clarity and precision of language | Information architecture and relevance filtering |
| Impact on output | High for individual responses | High for complex, multi-step workflows |
In practice, you need both. A precisely worded prompt in a poorly structured context will still produce inconsistent results. A rich context with a vague prompt will waste the information you provided. The two skills work together.
These five techniques produce the largest improvements in output quality for marketing tasks. Each builds on the previous one.
Tell the AI what expert it should act as before giving it the task. Role assignment calibrates the level of expertise, the vocabulary, and the assumptions the AI makes about what you need.
Example
You are a senior performance marketer with 10 years of experience managing Google Ads accounts with budgets over $500k per month.
Tell the AI exactly what format you want the response in. If you need a table, say table. If you need a numbered list, say numbered list. If you need JSON, say JSON with the schema. Unspecified format leads to inconsistent structure that is hard to use downstream.
Example
Present your findings as a table with four columns: keyword, match type, monthly spend, and recommended action. Sort by monthly spend descending.
Show the AI one or two examples of the output you want before asking it to produce the real output. This is the fastest way to get the AI to match your specific style, format, or quality bar.
Example
Here is an example of the ad copy format I want: [paste example]. Now write five variants for the following product following the same structure.
Ask the AI to reason through the problem step by step before giving its final answer. This improves accuracy for analytical tasks like campaign diagnosis or budget allocation recommendations.
Example
Before giving your recommendations, walk through your analysis step by step. Show how you reached each conclusion.
Tell the AI what NOT to do as clearly as what it should do. Constraints prevent the most common failure modes: excessive length, off-topic content, unsupported claims, and tone mismatches.
Example
Do not include generic best practices. Only include findings that are specific to the data I have provided. Keep the response under 400 words.
Here are four complete engineered prompts for common marketing tasks. Each applies multiple techniques from the section above. Copy and adapt these directly.
You are a direct response copywriter who has written ads for Google Search campaigns in the SaaS industry for 8 years. Write 5 responsive search ad headlines for the following product: [paste product description] Requirements: - Each headline must be under 30 characters - At least 2 headlines must include the primary keyword: [keyword] - At least 1 headline must include a number - Tone: confident, specific, no superlatives - Do not use exclamation marks Format: numbered list, one headline per line
You are a senior Google Ads analyst. Your job is to identify the highest-priority optimisation opportunities in a campaign. I am providing you with a search terms report for the past 30 days. [paste report] Analyse the data and identify the top 5 wasted spend patterns. For each pattern: 1. Name the pattern 2. Show the keywords or terms involved 3. Estimate monthly wasted spend 4. State the recommended action Sort by estimated monthly wasted spend, highest first. Do not include generic advice. Only reference data from the report I provided.
You are a keyword research specialist for B2B SaaS companies. Generate a keyword list for the following product category: [category] For each keyword provide: - Keyword phrase - Estimated intent (informational, commercial, transactional) - Funnel stage (awareness, consideration, decision) - Suggested match type (broad, phrase, exact) Include at least 5 keywords at each funnel stage. Format as a table with those four columns. Do not include branded terms.
You are a marketing analyst writing a weekly performance summary for a non-technical CMO. Here is the performance data for the week: [paste data] Write a summary with three sections: 1. What happened (2-3 sentences, key numbers only) 2. Why it happened (your analysis of the main drivers) 3. What we are doing about it (recommended actions for next week) Keep the total length under 200 words. Use plain language. No jargon. Do not hedge. State your analysis directly.
Most poor AI outputs are caused by one of three mistakes. Recognising them is the fastest way to improve your results.
Typing a query like "best ad copy for SaaS" produces generic results because it is a search engine query, not a task specification. Prompt engineering requires specifying the role, the data, the constraints, and the format. A search engine query asks for information. A prompt asks for work.
The fix
Replace queries with task specifications. Instead of "best ad copy for SaaS", write "you are a copywriter. Write five Google Ads headlines for [specific product] targeting [specific audience]. Headlines must be under 30 characters. Tone: direct and specific."
When you do not specify the output format, the AI chooses one for you. Sometimes it is appropriate. Often it is not. An unspecified format means you cannot reliably pipe the output into the next step of your workflow. You end up reformatting manually, which defeats the purpose.
The fix
Always specify the exact format you need: table with named columns, numbered list, JSON with schema, bullet points with two lines each, or prose with a specific word count. Include an example if the format is unusual.
Asking the AI to "analyse my campaign performance" without providing the data produces analysis based on assumptions. The AI will give you generic recommendations that apply to any campaign. The more specific data you provide, the more specific and actionable the output.
The fix
Paste the actual data into the prompt. For campaign analysis, paste the search terms report or the performance table. For ad copy generation, paste the product description, existing ads, and any customer feedback. The AI produces better work with better inputs.
Basic prompt engineering skills help you get better results from Claude Code, but you do not need to master it before starting. The skills on marketers.wiki include pre-written prompts for common marketing tasks. As you use Claude Code more, you will naturally develop an intuition for how to phrase instructions more precisely. Start using Claude Code on real tasks immediately, and refine your prompting as you go.
Prompt engineering focuses on the instruction itself: how to word a request to get the desired response. Context engineering is broader: it covers everything you put into the AI session including examples, reference documents, constraints, output format requirements, and the prompt. You can write a well-engineered prompt and still get poor results if the context is missing or wrong. Context engineering is the full system. Prompt engineering is one component of it.
The fundamentals, including role assignment, output format specification, and constraints, can be learned in a few hours. Getting consistently high-quality outputs for complex marketing tasks takes two to four weeks of regular practice. The fastest way to improve is to compare the outputs from vague prompts versus engineered prompts on tasks you already do manually, so you can immediately see the quality difference and calibrate what works.
The core techniques apply across all models, but each model responds differently to specific patterns. Claude tends to respond well to explicit output format requirements and constraints. It also performs better when given clear role framing and when told what not to include. ChatGPT and GPT-4 respond similarly to most techniques. When moving prompts between models, test and adjust rather than assuming direct portability. The same prompt may produce noticeably different results across models.
The skills library includes pre-built prompts for common marketing workflows. The Vibecoding guide shows how to use them in sequence.