Building AI-Powered Products

When to Use AI and When Not To → Data Strategy for AI Products → AI UX Design Patterns → Bài 4 — AI products landscape — Categories & examples → Bài 5 — LLM vs ML — When to use what → Bài 6 — Foundation models — GPT, Claude, Gemini, Llama compared → Bài 7 — RAG architecture — Build retrieval pipeline → Bài 8 — Fine-tuning — When, how, ROI → Bài 9 — Evaluations — Production-grade eval framework → Bài 10 — Guardrails — Safety, content policy, abuse prevention → Bài 11 — AI UX patterns — Conversational, augmented, copilot → Bài 12 — Latency & cost — Production tradeoffs → Bài 13 — MLOps for LLM — Deployment, monitoring, scaling → Bài 14 — Embeddings — Vector representations explained → Bài 15 — Building chatbot product — Architecture → Bài 16 — Building copilot product — AI in workflow → Bài 17 — Building AI agents — Beyond chat → Bài 18 — Product strategy for AI — Differentiation → Bài 19 — Build vs buy — Make vs API decisions → Bài 20 — User research for AI products — What's different → Bài 21 — Pricing AI products — Models that work → Bài 22 — AI features in existing products — Add-on strategy → Bài 23 — AI infrastructure layer — LangChain, LlamaIndex, frameworks → Bài 24 — Building AI features fast — Vercel AI SDK, Next.js → Bài 25 — Backend for AI app — Stack patterns → Bài 26 — Frontend for AI — Streaming UX patterns → Bài 27 — Testing AI products — Unit, integration, eval → Bài 28 — Data flywheel — Use AI usage to improve AI → Bài 29 — Working with PMs — AI roadmap collaboration → Bài 30 — Working with ML engineers — Collaboration patterns → Bài 31 — Compliance — EU AI Act, Nghị định 13, sector regulation → Bài 32 — Pricing & unit economics for AI → Bài 33 — Going to market — Launch strategies for AI → Bài 34 — Case study — Cursor product breakdown → Bài 35 — Case study — Perplexity AI search → Bài 36 — Case study — Harvey legal AI → Bài 37 — VN AI products — VinAI, FPT.AI deep dive → Bài 38 — Multi-tenant AI architecture → Bài 39 — Open source models — When self-host makes sense → Bài 40 — Iterating AI products in production → Bài 41 — AI for B2C — Consumer-focused patterns → Bài 42 — AI for B2B — Enterprise patterns → Bài 43 — Building MVP fast — Solo / small team → Bài 44 — Customer onboarding for AI — First impressions → Bài 45 — Pricing experiments — A/B testing → Bài 46 — Scaling AI products — From 100 to 100k users → Bài 47 — Fundraising AI startup — VC perspective → Bài 48 — Building defensibility — Moat for AI → Bài 49 — Future of AI products — 2026-2030 outlook → Bài 50 — Building team culture for AI → Bài 51 — AI safety in product — Ship responsibly → Bài 52 — Compliance for AI products (deep dive) → Bài 53 — Working with vendors — OpenAI, Anthropic, Google relationships → Bài 54 — AI product anti-patterns — Lessons from failures → Bài 55 — Building team for AI startup — Roles + roadmap → Bài 56 — Career as AI engineer / PM — Path 2026+ → Bài 57 — Building AI product in VN — Practical playbook → Bài 58 — Failure modes — When AI products fail → Bài 59 — Long-term thinking — Building lasting AI company → Bài 60 — Final synthesis — Building AI products in 2026 →