Key Takeaways

Before you build anything serious with LLMs, make sure you understand these foundations:

✅ Why Agents?

• Prompting doesn’t scale — you need structured, reusable logic you can debug and improve

✅ Why Stochastic Systems Need Rethinking

• LLMs aren’t predictable — you need ways to measure quality, compare outputs, and update safely
• Prompts need to move out of code — into config you can version, evaluate, and control

✅ Why You Need a Data Pipeline

• Your data isn’t ready — you need to extract, clean, and structure it before AI can use it effectively

Together, these give you the foundations for:

• Building smarter, more consistent workflows
• Avoiding one-off hacks that break under pressure — including hardcoded prompts that no one can trace or safely improve
• Gaining visibility into what your AI is actually doing

Most teams skip these. That’s why most prototypes don’t scale.

This is how to do it right.