The outcome

Operate a useful language feature with measured quality, safety, latency, cost, and failure behavior instead of relying on anecdotal prompts.

Upstage is a AI API platform for Solar language models, reasoning, document parsing and OCR, structured extraction, classification, embeddings, and agent workflows. Building language and document automation with Solar models, structured generation, document digitization, information extraction, classification, semantic retrieval, and agent APIs. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Define users, task, input classes, expected schema, quality and safety metrics, context distribution, reasoning need, tools, latency, throughput, cost, review, and rollback thresholds.

  2. 02

    Create a dedicated server key, record the API version and exact Solar model, context and output limits, reasoning and structured-output settings, rate limits, storage behavior, and prices.

  3. 03

    Build a frozen evaluation set with normal, long, multilingual, ambiguous, no-answer, conflicting, unsafe, malformed, injection, timeout, tool, and provider-error cases plus review rubrics.

  4. 04

    Run versioned prompts with explicit roles, schemas, token budgets, timeouts and retries; validate every field, authorize tools in application code, and capture redacted request and usage telemetry.

  5. 05

    Gate on quality, safety, tail latency, errors and task cost, canary a limited audience, monitor drift and spend, and retain the prior model, prompt, API version, and behavior for rollback.

Working standard

What good use looks like.

  • Pin API and model versions.
  • Validate every structured field.
  • Canary with a tested fallback.

Language, parsing, OCR, extraction, classification, and embedding endpoints have different inputs, limits, storage, and failure modes. Upstage's educational guidance says API data is not used for model training or service R&D, while asynchronous inputs may be stored until inference completes for up to three days and outputs for up to 30 days; console inputs may be handled differently. Confirm the current terms for the chosen service, keep keys server-side, minimize files, validate every extracted value, defend downstream systems from document injection, and delete temporary and derived data on schedule.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.