
Beginner-to-production guide to LLM APIs. Choose your first model, write your first API call, understand token pricing, and deploy with an 8-point production checklist.
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AI API insights, product updates, and tutorials from the team.

Beginner-to-production guide to LLM APIs. Choose your first model, write your first API call, understand token pricing, and deploy with an 8-point production checklist.

Stop 429 errors from crashing production. Three-layer architecture: client throttling, header-aware backoff, and predictive suspension with Python code.

A practical 2026 guide to integrating GPT-5.5, Claude Opus, Gemini, and DeepSeek through a single OpenAI-compatible API. Global edge latency, transparent pricing, security, and compliance-aware integration for developers worldwide.

Master the Claude API: Anthropic-native protocol, extended thinking, prompt caching at 90% off, tool use, and Claude Code integration. Production patterns.

Design human-AI handoff that doesn't frustrate customers. Six escalation triggers, context transfer checklist, agent experience design, and the #1 mistake that causes handoff failure.

Build a hybrid LLM architecture — self-hosted models for sensitive data, cloud APIs for frontier capabilities. Three production patterns with routing logic, cost models, and code.

Go beyond naive top-k retrieval. Implement hybrid search (dense+BM25), HyDE, Multi-Query, and Cross-Encoder reranking — with Python code and recall benchmarks.

12 strategies to cut LLM API costs up to 90%. Model tiering, prompt caching, batch API, multi-provider routing with real before/after data.

Apply classic software design patterns to LLM API integration. Factory for model selection, Strategy for prompts, Observer for streaming, Decorator for retry/logging —with Python code for each pattern.

Implement production-grade logging and audit trails for LLM API calls. Structured log schemas, common error code troubleshooting tree, and SOC 2/GDPR audit requirements.

Set up production LLM observability with OpenTelemetry GenAI conventions. Three-layer trace architecture, eval-as-span, tail sampling, and cost attribution —with runnable code.

Ten security baselines for LLM APIs: centralized vaults, scoped virtual keys, gateway architecture, automated rotation, budget caps, and audit logging.