llm api integration
Progress from zero to frontier with a guided depth ladder.
API Integration Patterns for LLM Features
A technical guide to shipping LLM features safely: request shaping, guardrails, retries, and observability.
LLM API Integration: A Complete Developer Guide
Everything you need to integrate LLM APIs into real applications: authentication, request patterns, streaming, error handling, cost management, and production best practices.
LLM API Caching Strategies: Stop Paying for the Same Answer Twice
Caching is the most underused optimization in LLM applications. This guide covers exact caching, semantic caching, prompt caching, and when each strategy applies.
LLM API Cost Optimization: A Practical Guide
LLM API costs can surprise you at scale. Here's how to profile, reduce, and control them without degrading quality — from prompt optimization to caching to model tiering.
Error Handling and Retry Patterns for LLM APIs
Production-grade error handling for LLM API integrations — retry strategies, fallback patterns, and graceful degradation.
How to Run LLM Evals in Production
Most LLM apps break quietly after launch. Here's how to set up practical production evals so prompt changes, model swaps, and retrieval drift do not surprise you.
LLM API Fallbacks and Failover: A Production Guide
How to design fallback paths for LLM systems without making behavior unpredictable: model failover, degraded modes, retries, and routing policy.
LLM Function Calling and Tool Use: A Developer's Guide
Function calling lets LLMs trigger real actions instead of just generating text. Here's how it works across major APIs, patterns that work, and pitfalls to avoid.
LLM API Integration Reliability Checklist — 20 Controls Before Production
A production checklist for LLM API integrations covering retries, guardrails, observability, and incident response.
Load Testing LLM APIs: Strategies for Capacity Planning and Performance
How to load test LLM APIs effectively — from designing realistic test scenarios and measuring the right metrics to capacity planning and handling the unique challenges of generative AI workloads.
LLM API Integration: Building Multi-Model Pipelines
How to build production multi-model LLM pipelines—routing strategies, fallback chains, orchestration patterns, cost optimization, and practical implementation with code examples.
LLM Observability: Tracing, Logging, and Debugging AI Applications
When your LLM app misbehaves in production, you need to understand what happened, why, and how to fix it. This guide covers observability patterns for LLM-powered applications.
LLM Prompt Caching: Cut Costs and Latency by 90%
Prompt caching is the single biggest optimization for LLM applications with shared context. This guide covers how it works across providers, implementation patterns, and the tradeoffs.
Managing LLM API Rate Limits and Quotas in Production
Rate limits are the most common source of production failures in LLM applications. This guide covers strategies for staying within limits, handling throttling gracefully, and scaling reliably.
LLM API Streaming: Building Responsive AI Interfaces
Beyond basic streaming: how to build AI interfaces that feel responsive using streaming APIs, progressive rendering, and smart UX patterns.
Streaming LLM Responses: Why It Matters and How to Build It
Streaming is how ChatGPT displays text as it's generated. Here's how it works under the hood, why it dramatically improves perceived performance, and how to implement it with the OpenAI and Anthropic APIs.
Structured Outputs from LLMs: JSON, Schemas, and When They Actually Work
Getting reliable structured output from LLMs is harder than it looks. This guide covers the techniques, tradeoffs, and failure modes — from prompt-based JSON to constrained decoding.
LLM API Versioning and Migration: Surviving Model Updates Without Breaking Production
Models get deprecated, APIs change, and behavior shifts between versions. Here's how to build LLM integrations that survive model updates without emergency deployments.
LLM API Webhooks and Async Patterns: Beyond Request-Response
How to build LLM-powered systems that go beyond synchronous request-response — covering webhook callbacks, job queues, long-running tasks, and event-driven architectures.