Master Prompts for Indie Hackers: How to Build AI Agents Without the Fluff
Spent the last few weeks battling brittle agent loops and broken LLM workflows. If you're still pasting 40-line "act as a senior expert with 20 years of experience" blocks into ChatGPT, it's time to change stacks. Models got smarter, but production failures now happen because your system has no contracts.
I wrote a practical, no-BS guide on how to structure master prompts that actually survive production:
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The 7-Part Anatomy: Moving beyond personality prompts to strict structural layers (roles, explicit constraints, output contracts).
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JSON Planning Loops: Forcing LLMs to output a structured task plan (Plan ➔ Act ➔ Observe ➔ Verify) before executing code.
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Context Engineering: Using high-signal XML delimiters (<user_input>, <codebase>) and strict anti-hallucination policies to eliminate guessing.
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PromptOps & Evals: How to run minimal test suites on a 0–2 grading scale so you don't fly blind when swapping models.
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Includes copy-pasteable templates for coding agents and growth/marketing research workflows.
Check out the full technical breakdown here:
https://dev.to/edo911/master-prompts-in-2026-stop-prompting-like-its-2023-52dh
