LLM integration tutorials and evaluation methods

Start with a minimal request, then validate protocol behavior, completion and task quality. These guides explain integration and evaluation methods; they are not model rankings or promises of performance, pricing or availability. Generation examples incur usage: confirm access and budget before running them.

1. Your first LLM API request

Discover a compatible model, send a server-side Chat Completions request, and validate the response without confusing LLM calls with media tasks.

2. Choose an LLM API protocol

Compare OpenAI, Claude and Gemini request shapes, authentication and response handling before moving an application between model routes.

3. Handle LLM streaming responses

Understand protocol-specific streaming, partial output, completion signals and disconnects before adding incremental text to an application.

4. Diagnose LLM API errors and retries

Separate authentication, contract, rate-limit and interrupted-response failures, and design bounded retries that avoid duplicate generation.

5. Compare LLMs with a task-based evaluation

Build a reproducible LLM comparison using task quality, failure rates, latency and observed cost instead of unsupported model rankings.

6. Estimate LLM cost and reconcile usage

Separate token estimates, current account pricing and final usage receipts when planning an LLM budget and comparing successful task costs.

Model contracts and current information

Use the Common API for discovery, model documentation for model-specific parameters and the complete API reference for endpoints. Do not generalize one model’s capabilities to every route.