The Challenge
Robert, founder of OfficeAutomated.com — a US-based tech company focused on doctors — wanted to adopt AI-driven development with Claude Code — but the gap between "AI can write code" and "AI is reliably shipping production features" is wider than most people realize.
The risks of getting it wrong:
- Runaway costs — without guardrails, AI API costs spiral fast when every keystroke triggers a completion
- False confidence — AI-generated code that looks correct but has subtle bugs, security holes, or architectural problems that compound over time
- No structured workflow — using AI as a fancy autocomplete instead of a development multiplier
- Team skepticism — developers unsure when to trust AI output and when to push back, leading to either over-reliance or under-utilization
He didn't need a course or a tutorial. He needed someone who builds production SaaS with AI tools daily to sit with him, watch how he works, and coach him in real-time.
The Solution
A focused coaching engagement — screenshare sessions where the client drives and I guide. Not lectures, not demos. Real work on his actual codebase with real-time feedback.
What We Covered
Session 1: Setup & First Assignment
- Installed and configured Claude Code with proper project setup
- Structured the codebase so AI tools have the right context — file organization, naming conventions, CLAUDE.md configuration
- Provided the first hands-on assignment to practice between sessions
Prompt Engineering for Development
- Structuring prompts that produce usable, production-quality code — not demos that need rewriting
- Context management — what to include, what to omit, how to scope requests so AI doesn't hallucinate architecture
- When to use AI for greenfield code vs. modifications vs. debugging — each requires a different approach
Cost Control & Security
- Setting up guardrails to prevent runaway API costs — token budgets, model selection, caching strategies
- Security practices for AI-assisted development — reviewing generated code for injection vulnerabilities, exposed secrets, insecure patterns
- Understanding the cost-quality tradeoff — when to use expensive models vs. when a lighter model is sufficient
Judgment: Trust vs. Review
- Building intuition for when AI output is trustworthy and when it needs human verification
- Recognizing common failure modes — confident-sounding wrong answers, subtly broken logic, solutions that work but don't scale
- Code review patterns specifically for AI-generated code
Format
- 1:1 screenshare sessions — client drives, I observe and guide in real-time
- Real codebase, real problems — no toy examples, every session works on actual production code
- Assignments between sessions — structured practice that accelerated learning
- Progressive autonomy — each session built on the last, with the explicit goal of independence
The Impact
- Full autonomy by session 5 — Robert went from zero AI tooling to independently shipping features with Claude Code
- Fantastic progress — after getting on track, Robert was able to take over and make significant progress on his own
- Sustainable AI adoption — not a one-time workshop that fades, but a structured ramp that built lasting capability
- Costs under control — deliberate model and token usage instead of "let it run and hope for the best"
Testimonial
"Yatharth was hired to help me with some coding activities. He is very knowledgeable and very responsive. He got me on track to a point where I was able to take over and make some fantastic progress. I will definitely be working with him in the future."
— Robert, Founder of OfficeAutomated.com (USA)