AI Dev Prompt Tricks That Make A Difference

Description:
In this video, I walk you through the prompt engineering strategy I use to guide AI models (like GPT-4o Mini or Claude) in building complex Next.js applications. You’ll learn the step-by-step structure of my prompts, how to avoid pitfalls with model hallucination, and specific tweaks—like specifying Next.js versions and referencing API responses—that ensure the AI produces code that actually works. If you’re tired of tangled prompts and buggy AI outputs, this is the streamlined method you need.

Chapters
00:00 – 01:09 | Introduction & Why Prompt Structure Matters

Overview of common pitfalls when using AI to code
Why step-by-step breakdowns and relevant data references are crucial
01:10 – 02:39 | The Step-by-Step Approach

How I divide the prompt into milestones (project setup, caching, routing)
Examples of real commands and key instructions
02:40 – 03:34 | Avoiding Model Hallucination

Why GPT-4 Mini or even DeepSeek struggle with large or ambiguous prompts
The benefits of “Sonet 3.5” or Anthropic Claude for complex coding
03:35 – 05:06 | Specifying the Right Next.js Version

Avoiding Next.js 15 due to model knowledge cutoffs
Ensuring your AI references the correct, stable framework docs
05:07 – 06:31 | Embedding API Docs & Responses in Your Prompt

Creating sections like “PixelBay Documentation” and “Example Response”
Letting the AI “see” JSON structures so it knows how to parse them
06:32 – 07:40 | Real-World Example & Tips

How to integrate actual data for a directory or business listings
Using a separate .md file for your roadmap and referencing it within AI tools
07:41 – 08:43 | Conclusion & Final Takeaways

Recap of the entire method: step-by-step tasks, pinned Next.js version, embedded data references
Closing thoughts on scaling and refining AI-coded projects
Suggested Hashtags
#AI
#PromptEngineering
#Nextjs
#GPT4
#GPT4o
#ClaudeAI
#Coding
#WebDevelopment
#APIDocs
#DeepSeek
#Automation

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