Category
本番LLMアプリ実装ガイド — 信頼できる生成AIの6本柱
生成AIの本番化は、プロンプトの巧拙ではなく『確率的で信頼できないLLMを、決定的なアーキテクチャで囲えるか』で決まります。本クラスタは、セキュリティ・MCP・評価・プライバシー・信頼性・キャッシュの6本柱を、貫く1つの設計原則・導入の順序・6本を1つのリクエストフローに合成する統合コードで束ねます。各柱は一次情報（OWASP/NIST/MCP仕様/APPI・GDPR/AWS・Google SRE）に準拠した深掘り記事で、本番運用に耐える設計を提供します。
7 articles in total
Foundational guide
Foundational guide (start here)
The Complete Guide to Production LLM App Engineering 2026: The 6 Pillars of Trustworthy Generative AI
An implementation roadmap for turning generative AI/LLMs from a 'demo' into a 'trustworthy production system.' The six pillars — security, MCP (tool connection), evaluation, privacy, reliability, and caching — with the single design principle that unifies them, the adoption order, and integrated code, as a map to each deep-dive article.
Related practical articles
- 生成AILLMキャッシュコスト最適化パフォーマンス
Production LLM App Caching Strategy 2026 | Prompt Caching + Semantic Caching to Cut Latency & Cost
An implementation guide to two kinds of caching that cut latency and cost for generative-AI/LLM apps: provider prompt caching (Anthropic cache_control, OpenAI automatic, Google context caching) and application-layer semantic caching (embedding similarity, pgvector) — with when to use which, false-hit mitigation, TTL design, and real TypeScript code.
7 min read - 生成AILLMセキュリティプライバシーコンプライアンス
Production LLM/AI App Data Privacy & Compliance Guide 2026 | PII Minimization, Provider Data Use, APPI/GDPR
A practical guide to guaranteeing data privacy and compliance for production LLM/AI apps through architecture. Covers the major API providers' data-use and retention policies (OpenAI/Anthropic/Google/Bedrock/Vercel), PII minimization and redaction, zero data retention, data residency, and APPI (Art. 28 cross-border transfer) and GDPR (Art. 28 DPA / data minimization) — with real TypeScript code.
8 min read - 生成AILLM評価テストLLM-as-judge
LLM App Evaluation & Testing Guide 2026 | LLM-as-Judge, Regression Testing, CI Gates
A practical guide to evaluating and testing production LLM apps so they don't break. How to measure non-deterministic output, how to build eval datasets (golden sets), when to use deterministic grading vs LLM-as-judge, RAG metrics (RAGAS), and how to gate regressions in CI — with primary-source-faithful TypeScript code.
9 min read - 生成AILLM信頼性回復性冪等性
Production LLM App Reliability & Resilience Guide 2026 | Multi-Provider Fallback, Retry, Circuit Breaker, Idempotency
A reliability & resilience design guide for keeping production generative-AI/LLM apps from falling over. Timeouts, retries with exponential backoff + jitter (transient failures only), circuit breakers, multi-provider fallback (AI SDK / Vercel AI Gateway), idempotency, and rate limiting — with primary-source-faithful TypeScript code.
8 min read - 生成AILLMセキュリティOWASPプロンプトインジェクション
Production LLM/AI App Security Guide 2026 | OWASP LLM Top 10 & Prompt Injection Defense
A security guide for developers shipping generative-AI/LLM apps to production, aligned with the OWASP Top 10 for LLM Applications (2025) and NIST AI RMF. Defend against prompt injection (direct/indirect), RAG data leakage, excessive agent permissions, and improper output handling — with real TypeScript/Zod/Vercel AI SDK code and defense-in-depth architecture.
13 min read - MCPModel Context Protocol生成AILLMAIエージェント
MCP (Model Context Protocol) Production Server Guide 2026 | Architecture, Transports, Authorization, Security
A practical guide to building production-grade Model Context Protocol (MCP) servers. Covers the host/client/server architecture, stdio/Streamable HTTP transports, the correct official TypeScript SDK (v1) API, OAuth 2.1 authorization (RFC 9728/8707, PKCE, token audience validation), tool safety design, and the official security best practices — with primary-source-faithful TypeScript code.
10 min read