AI Agent Coding Tip (for real-world engineering)

💡 AI Agent Coding Tip (for real-world engineering)

Most developers use AI agents like this:

👉 “Build me a feature…”

And then wonder why the output is inconsistent.

🔥 The real unlock:

👉 Switch from “instruction prompting” → “spec-driven prompting”

Instead of this:

❌ “Create an API for user management”

Use this:

✅ “You are a senior C# engineer.

Build a REST API with:

• .NET 10 minimal API
• Endpoints: CreateUser, GetUserById
• SQL Server (Dapper)
• Validation rules: email required, unique
• Include error handling + logging
• Output production-ready code only”

🧠 Why this works:

• Reduces ambiguity
• Forces structured thinking
• Produces consistent, reviewable output
• Makes AI behave like a junior dev following a spec

⚙️ Pro tip (game changer):

👉 Always include:

• Tech stack
• Constraints
• Expected output format
• Edge cases

🚀 Advanced pattern:

Break your workflow into agents:

1. Planner → defines spec
2. Builder → generates code
3. Reviewer → validates & refactors

👉 This mimics real engineering teams.

AI agents are not magic.

They’re deterministic systems that reward precision.

Curious — are you using spec-driven prompts or still ad-hoc prompting?

#AI #AIAgents #CSharp #SoftwareEngineering #DevTools #LLM