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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
AI Feature Flags Cheatsheet: Rollout, Rollback, Observe
A reference for engineering teams who learned the hard way that UI feature flag tools don't catch model regressions. Gating rules, rollback triggers, and the signals worth wiring up.
LangChain vs. LlamaIndex: Pick One for Your AI Product
Your prototype works. Now you have to decide whether to bet a real production system on LangChain or LlamaIndex. This is the comparison that focuses on the dimensions that actually break under load, not toy benchmarks.
Your AI Feature Has a Trust Problem, Not an Accuracy Problem
Your model is 92% accurate. Your acceptance rate is 11%. The fix is not a better model. The fix is making the output legible at the moment a user has to act on it.
Managed AI Infra vs. Self-Hosted: Pick One
Your managed inference bill tripled and self-hosting looks cheaper on paper. It usually isn't. Here's the framework that actually decides it — and why the answer is a staffing question, not a compute one.
How to Run an A/B Test on an AI Feature Without Lying to Yourself
Your AI feature's A/B test showed a lift. Then it flatlined after rollout. Here's a playbook for running experiments on adaptive systems without lying to yourself about what the numbers mean.
How Vahak Onboarded 800K Trucks Without Breaking Its AI
When a transport marketplace adds tens of thousands of new carriers a month, the AI matching model doesn't just slow down — it gets confidently wrong. Here's the architectural call that separated cold supply from warm supply and stopped the degradation.
5 Mistakes We Made Shipping AI to a Live Transport Marketplace
Your route matching model is live, acceptance rates are flat, and nobody on the team can tell if the problem is the model, the data, or the marketplace itself. Five mistakes we keep seeing on freight AI deployments — and how to recover before carriers churn.
ML Model Versioning Cheatsheet: Weights, Data, and Code
A model version isn't a file. It's a tuple of weights, data snapshot, preprocessing code, hyperparameters, and eval thresholds — pin all five or your rollback is a lie.
Questions to Ask Before Hiring an AI Healthcare Data Partner
Most healthcare AI vendors can recite the HIPAA checklist. Far fewer can show you a production HL7 parsing failure and the architecture change they made because of it. Here are the questions that surface the difference.
Guardrails for LLMs: Why Output Validation Is Its Own Layer
Prompt engineering tells the model what you want. Guardrails enforce what your system will actually accept. The distinction matters more than most teams realise until a bad output reaches a paying customer.
Vector DB vs. Postgres pgvector: Pick One for Your AI Product
Your infra lead says pgvector won't scale and you need Pinecone or Weaviate. They might be right. They're also probably wrong for the reasons they think. Here's the framework that actually matters.
Fine-Tune an Embedding Model on Your Own Docs in 6 Steps
Your RAG pipeline keeps returning confidently wrong passages, and you've already exhausted chunking and re-ranking tricks. The defect is in the embedding model itself — here's how to fix it with 500 pairs from your query logs.