Writing
This is my AI masterclass series — built from real work in LegalTech. I’m documenting the journey from zero AI knowledge to building reliable systems with LLMs, embeddings, RAG, SQL, .NET, and workflow automation tools like n8n. If you’re new, you belong here — I’ll keep it practical and respectful.
If you’ve never built anything with AI, start here. No jargon. No hype. Just clarity.
A clear mental model for LLMs, what they can do, what they cannot, and how to think like a builder.
A simple 5-part framework + templates you can reuse for summaries, extraction, Q&A, and structured output.
How to choose models without guesswork — based on your goal, latency needs, and budget reality.
This is where AI becomes a system — not just a chat box. You’ll understand the building blocks and how they connect.
How text becomes numbers (vectors) and why that unlocks search that feels “smart”.
The pattern behind grounded, reliable AI: retrieve trusted context first, then generate.
From UI to backend to vector store to audit logs — the minimum architecture that still works in reality.
Real systems, real trade-offs. These are patterns I use to ship AI inside operational workflows.
Turn project learnings into searchable memory with filters, permissions, and audit trails.
Convert plain English into safe, validated, explainable SQL — then return charts and summaries.
Orchestrate LLM calls, database lookups, approvals, and notifications — without overengineering.
This is where most AI projects break: security, evaluation, and cost. Here’s how to ship without chaos.
RBAC, tenant isolation, retrieval filters, prompt-injection defense, and auditability — explained practically.
Measure groundedness, retrieval quality, hallucination risk, and regressions — without heavy tooling.
Token costs, caching, model routing, latency, failure modes — and what I’d redesign from day one.
Let’s scope a focused initiative that ships in weeks, not quarters.