生成AI・LLM・RAG の本番実装ガイド

A getting-started guide to pgvector for beginning vector search in PostgreSQL. With real code faithful to the official documentation, it explains in the shortest path the enable procedure on each of Docker, Supabase, AWS RDS/Aurora, Neon, Google Cloud SQL/AlloyDB, and Azure, the permissions and common errors of `CREATE EXTENSION vector`, and your first table creation, INSERT, distance operators, kNN search, and HNSW index.

Which vector-search foundation should you pick? This compares pgvector (a PostgreSQL extension) against dedicated vector DBs (Pinecone, Qdrant, Weaviate, Milvus, Chroma) across seven axes — operational load, transactional consistency, scale ceiling, latency, metadata filtering, cost, and lock-in. Including a scaling strategy with pgvectorscale (StreamingDiskANN), it's a tech-selection guide to support the decisions of buyers and architects.

A tuning implementation guide to finishing PostgreSQL + pgvector vector search to production quality. We explain — in real code faithful to the pgvector official documentation — the HNSW/IVFFlat parameters (m, ef_construction, ef_search, lists, probes) and how to measure recall, how to cut memory with halfvec, binary quantization, and subvector, the iterative scan (iterative scan, 0.8.0+) that prevents over-filtering, and speeding up the build and operations.

Do you think LLM structured output (JSON) is safe if you use constrained (guided) decoding? What constrained decoding guarantees is 'syntactically valid JSON,' not 'semantically correct values.' Failures don't vanish; they change shape. It explains the production design of schema validation + business-rule validation + repair retry + fallback, from the real example of running structured AI output in production and a Zod implementation.

A guide to designing LLM-agent tool calls (function calling) at production quality. The Claude/OpenAI tool-use loop, tool definitions via JSON Schema, input validation at the boundary, idempotency, retries, timeouts, observability, and prompt-injection defenses—all explained in real code.

An implementation guide to building production RAG with PostgreSQL + pgvector. We explain in real code the distance operators (<-> / <#> / <=>), the choice between HNSW and IVFFlat, how to decide the embedding dimension, hybrid search of vector × full-text, chunk design, idempotent ingest via content hashing, and re-embedding operations on a model change.

A full record of the design that raised a GPU-inference pipeline — which fully automates, just by uploading a video, audio separation → transcription → translation → multilingual dubbing → mouth synchronization — up to a quality that withstands production operation. It explains, at the implementation level, resuming from spot interruption, an about-40% GPU-cost reduction via speech-segment detection, isochrony control, and hardening the diffusion model's OOM/hallucination.

The definitive guide to implementing production-quality AI features with the Claude API and Vercel AI SDK v6. Structured output, tool use, streaming, agents, prompt caching, cost optimization, observability, and security explained in real code compliant with the official documentation. Also covering model specification via the AI Gateway and fallback.

Overcoming the triple suffering that cloud-LLM dependence produces — physical latency, privacy breakdown, and economic unsustainability — with on-device inference on WebGPU, strong eventual consistency via CRDTs, and an autonomous-agent mesh via the Actor model. We design this next-generation Local-First Agentic architecture, going as deep as type-puzzle-grade TypeScript, WGSL compute shaders, and a zero-trust sync protocol.

A guide to building a RAG system at production-operation level, not a verification environment. The 5 hallucination countermeasures, accuracy-evaluation methods, and cost-optimization strategies implemented with LangChain + Pinecone + FastAPI, explained with real code.