TokSpan Blog

Move beyond "write better instructions." A production-grade guide to prompt versioning, automated optimization with DSPy, provider-specific quirks, and CI/CD for prompts —with code.

Build a production-ready RAG pipeline with LLM APIs. Covers chunking, embeddings, vector search, reranking, and evaluation —with runnable Python code for every step.

Get consistent JSON from every LLM API. Compare structured output implementations across OpenAI, Anthropic, and Gemini — constrained decoding, schema definitions, and cross-provider wrapper code.

The 2026 LLM API stack: model providers, gateways, observability, caching, guardrails, agent frameworks. Stacks for solo, startup, and enterprise.

Production architecture for running Multiple AI Models in one app. Fallback chains, 5 routing strategies, unified observability, and a complete Python application with code.

Choose the right vector database for your RAG pipeline. Head-to-head comparison of Pinecone, pgvector, Weaviate, and Qdrant on performance, cost, and operational complexity.

Direct API costs more, limits more, and fails more than aggregation. 3.6M monthly visits, TCO analysis, and workflow data explain the 2026 shift.

40–60% of e-commerce traffic lands when your support team is asleep. Learn how AI-powered 24/7 advisory boosted night conversion by 34% and cut support costs by 85%.