Prompting is Easy.
Debugging is Hard.
We don't teach "prompt engineering". We teach you how to architect, debug, and supervise autonomous AI systems when things go wrong.
The "Copy-Paste" Crisis
The Skill Gap: Traditional developers know syntax. "Vibe coders" know how to ask ChatGPT. But who knows what to do when the AI writes a function that looks right but creates a race condition?
Most enterprise AI initiatives fail because the team lacks AI Literacy. They treat the LLM as magic, not as a stochastic component that requires distinct engineering principles.
You don't need more "coders". You need AI Supervisors who understand context windows, token limits, RAG retrieval failure modes, and agent loops.
What Simple Prompting Misses:
- ×Debugging non-deterministic outputs
- ×Optimizing token usage/cost ($$)
- ×Handling 'Context Window' overflow
- ×Preventing 'Prompt Injection' attacks
- ×Architecting multi-agent orchestration
From Magic to Science
The Challenge: An enterprise engineering team was stuck. Their internal "AI Co-pilot" was hallucinating 30% of the time, and the team was just trying different prompts hoping it would fix it. They treated the LLM like a magic box, not a software component.
The Jini Solution: We ran a 2-week intensive "AI Supervisor" Bootcamp. We taught them to build evaluation pipelines, measure confidence intervals, and architect multi-stage reasoning loops. They moved from "prompting" to "engineering".
Curriculum for the AI Era
Transforming engineers and PMs into functional AI Supervisors.
Learn to diagnose why an agent failed. Was it the retrieval? The system prompt? The temperature setting? We teach the scientific method for AI.
Move beyond simple chatbots. Learn to design complex, multi-step workflows where agents plan, tools execute, and supervisors review.
For PMs: how to scope AI projects realistically, define success metrics for non-deterministic software, and manage "vibe coding" teams.