AI Prompt Engineer & OpenAI Integration Specialist

What’s your process for designing a high-quality prompt that consistently returns accurate, usable results from GPT-4 or GPT-4o?

What are the key limitations of GPT-4 or GPT-4o that you consider when designing prompts or workflows?

How would you structure a prompt to analyze this Google Ads JSON (or CSV) data and return the top 3 optimization suggestions?

Have you used OpenAI’s API in a production app? Describe how you managed token limits, retries, or failures.

What are system messages, and how do you use them effectively in multi-step interactions?

Have you worked with function calling? Show an example of how you’d set up a function to extract ad improvement suggestions.

Describe a time you used prompt chaining or multi-step workflows to improve accuracy. What was the input, logic, and final output?

If GPT-4 is giving inconsistent answers, how would you structure your process to increase accuracy and reliability?

Imagine you have a client’s landing page copy, Google Ads search terms, and conversion data. How would you format and feed this into GPT to give context-aware analysis?

Have you ever worked with embeddings or vector search (e.g. Pinecone, Weaviate)? If yes, describe how you used them to provide context to GPT.

Have you collaborated with developers to build a GPT-powered app? What kind of documentation or support do you provide for them?

If a dev gives you a data structure like this [insert JSON], what questions would you ask to turn it into a great prompt?

Describe a creative use of GPT you’re proud of — what problem did it solve, and how?

What are some common prompt engineering mistakes you see others make? How would you improve them?