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> ## Documentation Index
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# Medium

**[Medium](https://daily.dev/sources/medium_js)** · 7 min read · 0 upvotes · 0 comments

## Summary

Building a healthcare app for sickle cell disease management revealed that AI coding agent failures stem from context failures, not prompt failures. The author developed a three-layer context engineering system: a PRD with explicit non-goals and adversarial review, an AGENTS.md file defining locked decisions and behavioral rules, and a set of granular rule files for domain-specific constraints. This 50-page structured context prevented the agent from swapping technologies, pulling in unplanned features, or guessing at ambiguous requirements. The safety layer was built as a vendor-agnostic translator so AI model swaps don't affect safety behavior. The core lesson: reliable AI-generated code depends on deliberately designing the entire information environment the agent operates in, not on crafting better prompts.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@omotola.a.bello/the-prompt-was-never-the-problem-what-building-a-healthcare-app-taught-me-about-context-f1c363615b57>

## Questions this post answers

### What is context engineering and how does it differ from prompt engineering for AI coding agents?

Context engineering is the deliberate design of the entire information environment an agent operates inside, as opposed to prompt engineering, which focuses on wording a single instruction well. A three-layer structure works well: a PRD stating the problem, non-goals, and success metrics; an AGENTS.md file locking the tech stack, architecture, and a 'stop and ask, never guess' rule; and separate rule files for domain-specific detail like database schemas and security models.

_Anyone iterating on agent instructions instead of project context can find deeper workflow breakdowns on daily.dev._

### How can I prevent an AI coding agent from silently guessing on ambiguous requirements or swapping locked technology choices?

Put an explicit rule in an AGENTS.md file stating what is locked (tech stack, architecture, database schema) and instructing the agent to stop and ask rather than guess when it hits genuine ambiguity. Without this, an agent working inside an agentic IDE will confidently swap a locked technology for one it considers more elegant, or resolve ambiguity by guessing without telling you.

_Developers hardening their agent instructions against silent drift can track more context-engineering patterns on daily.dev._

## Similar posts on daily.dev

- [I Stopped Prompting AI Better. I Started Giving It Better Context.](https://daily.dev/posts/i-stopped-prompting-ai-better-i-started-giving-it-better-context--tvjfoo9wh) · Medium · 0 upvotes · 0 comments
- [Your SDLC is your context engineering](https://daily.dev/posts/your-sdlc-is-your-context-engineering-w6tqmjmyc) · LeadDev · 15 upvotes · 1 comments
- [Agentic Development: We Handed Off the Code, Not the Job](https://daily.dev/posts/agentic-development-we-handed-off-the-code-not-the-job-2ou02tzdx) · Atomic Spin · 0 upvotes · 0 comments

---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#architecture](https://daily.dev/tags/architecture), [#healthcare](https://daily.dev/tags/healthcare), [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#context-engineering](https://daily.dev/tags/context-engineering)

[View this post on daily.dev](https://daily.dev/posts/medium-sk63xropu)

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