Hey! I’m building an AI tool that generates 30-day challenge plans based on self-help books, using GPT-4o via the OpenAI API. The tool already works — it takes a user’s goal, emotional blocker, and book title, and generates a 4-phase challenge sequence. Each phase has 5-10 days of actionable tasks. However, even with a strong prompt and full context passed in, the output still feels too generic and lacks emotional depth or progression. Here’s what I’m looking for: The Goal: Help re-engineer the prompt (or structure) so the GPT-4o API produces more emotionally personalized, engaging, and progressive challenge sequences — closer to what you’d get from ChatGPT UI. What You’ll Work With: The current generation prompt (I’ll share it) How we pass in user inputs (goal, blocker, book) Phase structure (4 phases: quick wins, real-world application, mastery, long-term reinforcement) Ideal Skills: Experience engineering prompts for OpenAI’s GPT-4, GPT-4o, or Claude Understanding of emotional tone and personalization via prompt construction Familiarity with stateless API behavior (and how to simulate memory/context manually) Optional Bonus: If you have ideas for how to split up generation in steps (e.g. generate Phase 1 → reuse that context to generate Phase 2), that’s a huge plus. Deliverable: An improved version of the prompt that works better within GPT-4o API (Optional) A recommended structure for how we should pass prior phase context between generations Budget: Flexible depending on experience Timeline: ASAP — I’m ready to go Please include examples of previous prompt work if possible. I've included side-by-side examples of what I’m working with so you can quickly see the gap I’m trying to close.
Keyword: Python
Price: $200.0
AI Model Training Prompt Emotion Recognition Deep Learning Machine Learning Prompt Engineering ChatGPT ChatGPT API ChatGPT Prompt
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