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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
5 Mistakes We Made Shipping AI to a Live Pharmacy Marketplace
A field-level post-mortem on what breaks when AI substitution, routing, and recommendation features hit a real e-pharmacy catalog. Five specific mistakes, the symptoms you'll see in production, and how to recover without rolling everything back.
Ship a Drug Interaction Alert With a Local LLM in 7 Steps
A runnable tutorial for CTOs at e-pharmacy startups who need drug interaction alerts without sending patient data to OpenAI. Uses Mistral 7B locally, a versioned interaction dataset, and citation-grounded extraction.
How KarroFin Scaled AI Credit Scoring Without Killing Approval Rates
KarroFin's credit model wasn't broken. No alerts, no errors, no engineering fires. But approval rates were quietly compressing at scale — and the fix wasn't where the data science team was looking.
Sync vs. Async AI Inference: Pick the Right Model for Your Product
Most AI features ship synchronously because that's how the tutorial was written. By the time latency, cost, and reliability start compounding, the inference mode has become a UX contract you can't quietly break. Here's how to pick correctly the second time.
LangChain vs. LlamaIndex vs. Raw API: Pick One
Three days into a prototype, every LLM orchestration framework looks the same. Here's how to pick between LangChain, LlamaIndex, and a raw API wrapper based on where you want to own the complexity — not which one had the best quickstart.
Feature Stores Explained: Why Your ML Models Stale Out
Your credit risk model nailed backtesting but production accuracy keeps slipping. The culprit is rarely the model — it's a silent mismatch between how features are computed at training time and at inference. Here's what a feature store actually does about it.
How to Audit an AI Feature Before It Ships to Production
Your AI feature passed internal demos. That's not the same as being ready for real users. Here's the pre-ship audit playbook to either confirm your fear or clear the launch.
AI Observability Stack: What to Monitor and When
Your APM dashboard says the AI feature is healthy. Your users disagree. Here's the observability stack that catches what p99 latency and error rate structurally cannot — drift, hallucination, prompt regression, and feedback loop poisoning.
Your RAG Pipeline Isn't Failing. Your Chunking Strategy Is.
Most broken RAG pipelines aren't broken at the retrieval layer — they were broken at ingestion, when documents were split without respecting semantic boundaries. Here's why chunking is the silent failure mode no metric catches.
Questions to Ask Before Hiring an AI Development Partner for Healthcare
Every AI vendor claims healthcare experience. Here are 15 specific questions that separate teams who have actually shipped under HIPAA, HL7, and clinical scrutiny from those who built a wellness app and are overstating their credentials.
In-House AI Team vs. AI Development Partner: Pick One
You have 30 days to decide: hire two senior ML engineers or engage an AI development partner for your first core feature. Here's the decision framework that actually matters — and the one axis most founders get wrong.
5 Mistakes We See Teams Make Shipping AI to Thin-File Users
Most thin-file AI lending models don't fail because the architecture is wrong. They fail because the team never audited what happens after the first batch of rejections starts retraining the model. Here are the five failure modes we see most often.