Building and operating APIs powered by large language models.
View All TagsAI-powered APIs don't follow the same request-response flow as traditional REST APIs. Here's precisely what changes, and why it matters.
Architecture patterns for production LLM-backed APIs: request design, streaming, retries, fallback chains, caching, and cost control.
Learn how embedding APIs turn text into vectors, which distance metric to use, how batching affects cost, and how embeddings power search and RAG.
Cut LLM API spend without sacrificing output quality. Learn semantic caching, model routing, and prompt compression, enforced at the AI Gateway.
Compare OpenAI, Anthropic, and Gemini APIs on latency, cost, structured output, and tool calling with real pricing math for production workloads.
RAG endpoint patterns, vector DB integration, hybrid retrieval, and evaluation, plus where governance fits when your LLM calls out to your own data.