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I read through Anthropic’s official Claude prompting documentation so you don’t have to and then I put it to the test by updating a real app live. I break down 3 specific prompting principles straight from the docs and prove they work by improving my Swipe for Dinner app in real time: • Adding the right context so Claude actually understands the why • Reducing hallucinations by forcing file level grounding • Getting better research results with clear success criteria and verification You’ll see: Meal cards upgraded with real metadata like cook time, difficulty, kid friendly, and cuisine Claude locating and reading actual project files instead of guessing Web research that pulls real recipes from real sources A live fix when Claude’s output doesn’t match the source and how to correct it If you’re using Claude Code and not applying these practices, you’re leaving performance on the table. Like, subscribe, and comment if you want me to keep shipping and iterating on this app in public. Anthropic Claude prompting docs: https://platform.claude.com/docs/en/b... Swipe for Dinner app waitlist: https://bigdogbuilds.ai/dinner --------------------------------------------------------------------------------------------------------------------------- Timestamps 00:00 Intro 0:46 Claude documentation 02:29 Tip 1: Add context, explain the why 05:09 Tip 2: Prevent hallucinations, have Claude read before acting 06:56 Tip 3: Better research with success criteria and verification 12:22 Recap of the 3 tips