AI-accelerated development
How I actually build with AI — what gets generated, what gets thrown away, and the review discipline in between.
Prompt Versioning and Why Prompts Belong in Git
A prompt is code. Storing it in a dashboard means no review, no history, and no way to tell what changed when quality dropped.
Semantic Search That Understands What People Meant
Why keyword search fails on natural questions, what semantic search costs to add to an existing product, and when it is the wrong answer.
Evaluating an AI Feature Before You Trust It
A test set, a scoring method and a regression check — so a prompt change cannot quietly make your answers worse without anyone noticing.
Rate Limiting an AI Feature So One User Cannot Drain the Budget
Per-user, per-org and global limits for AI endpoints — and why degrading gracefully beats returning an error the user cannot act on.
Document Extraction Pipelines That Survive Messy Input
Turning PDFs, scans and inconsistent forms into structured data — with validation, confidence scores and a human review path that makes it trustworthy.
Provider-Agnostic AI: Swapping Models Without a Rewrite
The adapter layer that let a production app move between Claude, GPT and Groq by changing one file — and the four things it has to normalise.
Semantic Search With pgvector Before You Buy a Vector Database
Postgres handles semantic search for most products. When pgvector is enough, how to index it, and where a dedicated vector database earns its cost.
Streaming AI Responses Without Thrashing Your UI
Token streaming that stays smooth: batching updates, avoiding layout shift, and handling a dropped connection mid-answer.
Per-User AI Cost Logging: Finding the Account Burning Your Budget
What to log on every model call, and the query that finds the one user costing more than their whole plan is worth.
AI Booking Systems for Clinics, Salons and Trades
A booking assistant that checks real availability and books the slot — and the architecture rule that stops it double-booking your calendar.
Validating Every LLM Tool Call With Zod
The model will eventually return a malformed tool call. The Zod schema layer that turns that from a corrupted database into a logged retry.
Why AI Is a Module on a Real App, Never a Stack of Its Own
AI-first architectures fall over the moment you need auth, billing or an audit trail. The case for bolting AI onto a boring, well-built application.
How Much Does It Cost to Build an AI Feature in 2026?
What an AI chat, search or extraction feature actually costs to build and to run — build fees, token spend, and the infrastructure most quotes leave out.
Shipping an MVP in 19 Days Without Shipping Garbage
A real 19-day build of an AI research assistant: what got scoped out, what got generated, and the four things that never get cut no matter the deadline.
How I Actually Build With AI — and Where I Still Do the Work
The honest version of an AI-accelerated workflow: what gets generated, what gets rewritten, and the review discipline that keeps fast from meaning fragile.
RAG That Actually Answers: Chunking, Reranking and Citations
Most bad AI answers are bad retrieval, not a bad model. The chunking, reranking and citation changes that moved accuracy most on a production assistant.