#llmapi

A 2026 architecture for an AI support bot you can put in front of customers: RAG grounding, real guardrails, a faithfulness judge, and confidence-based escalation.

A practical 2026 guide to LLM API errors: which status codes to retry, how 429 rate limits work, backoff with jitter, and what the SDKs already do for you.

LLM API observability in 2026: instrumenting with the OpenTelemetry GenAI conventions, the four signals to alert on, and Langfuse vs Helicone vs Phoenix.

How OpenAI, Anthropic, and Gemini implement structured outputs in 2026 — strict mode, JSON Schema subsets, and constrained decoding vs. shape-only guarantees.

A numbers-first 2026 guide to cutting LLM API bills: how prompt caching, batch APIs, and model routing stack across Anthropic, OpenAI, and Gemini.

Claude vs GPT vs Gemini APIs in 2026: per-token pricing by tier, context windows, features, and a decision framework — every figure cited to an official page.

Originally published at norvik.tech Introduction Explore the intricacies of LLM API...

I benchmarked Claude Haiku 4.5, Claude Sonnet 4, GPT-4.1, GPT-4.1 Mini, and Gemini 2.5 Flash for TTFT, throughput, and end-to-end latency — with a cost-latency decision matrix for production builders.

Four cost optimization techniques for Gemini 2.5 Flash API, discovered through hands-on experiments. Covers Thinking token control, Context Caching, Flash vs Flash-Lite selection, and Batch API — with real measured data.

Claude Haiku 4.5 hits 73.3% SWE-bench at $1/M input tokens. This guide explains when Haiku beats Sonnet, how to cut costs 95%, and which workloads to avoid.

Нейросети заменяют дорогих исполнителей в 18 типовых задачах бизнеса: от копирайтинга и SMM до...

LLM API на Python — это не один навык, а пять взаимосвязанных: считать токены и предсказывать...