LLM application tutorials: APIs, streaming, and output validation

Who is this for, and how should you start?

For learners with basic development experience moving from using AI tools to building LLM applications. This path focuses on application engineering.

Start with one minimal model request

Define the model, input, output limits, and timeout. Complete one server-side request before connecting an interface. Keep keys on the server and send only task-relevant material from the client. Then observe permissions, rate limits, timeouts, and service failures and display clear states.

How should streaming and structured output be verified?

Streaming needs cancellation and an explicit interrupted state. Partial output must not be treated as a validated final result. Structured output requires format constraints and server-side checks for required fields, types, enums, and length. Report parsing failures instead of guessing business values.

Turn a model call into a sustainable feature

Set task budgets, output limits, and per-user quotas. Bound retries to recoverable failures and record usage and errors. Next, add sourced retrieval and evaluate answers with the same question set, preserving failures and improvements.

Learn in sequence

4 learning units are available. Read, practice, and keep your verification records.

1. Model APIs and requestsL3 · About 12 min
2. Implement streaming interactionsL3 · About 15 min
3. Structured output and validationL3 · About 18 min
4. Errors, usage, and cost controlL3 · About 21 min

Test your methods in a project

From documents to evidence-based answers. Follow the project steps and verify actual deliverables.

Common learning questions

Do I need to train a model first?

This learning path begins with calling model APIs and building applications. Model training, fine-tuning, and algorithm research are separate directions and are not prerequisites here.

Can returned JSON be saved immediately?

Validate its format and business meaning on the server. Valid JSON can still omit fields, exceed limits, or contain unsupported content. Show a clear failure when validation does not pass.