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Validation/Phase_08/18 min build

Self-Correcting Content Localization

A quality-assured translation pipeline utilizing 8 nodes. It attempts localization with a high-speed LLM, enforces formatting via tool calls, and routes failures to a heavy-duty reasoning model for self-correction.

Execution_Steps

  1. 01

    Initial Translation Attempt

    Connect an `input` node to an `llm_call` node configured with "gemini-1.5-flash" to optimize for speed and low cost.

  2. 02

    Schema Validation

    Attach a `tool_call` node directly after the Fast Translator. Configure it to enforce a strict JSON localization schema. This acts as our programmatic Quality Assurance.

  3. 03

    Quality Routing

    Insert a `router` node. Configure it to evaluate the success of the tool call. Connect the "Pass" edge directly to an `output` node.

  4. 04

    The Correction Fallback

    Route the "Fail" edge to a heavy reasoning model (`llm_call` using "claude-3-7-sonnet"). Attach a final `tool_call` and `output` to complete the self-correction loop, ensuring malformed translations are always caught and repaired.

Expected_Metrics

P50_LATENCY:< 3800ms
COST_SAVING:55.0%
SLA_LIMIT:6500ms

Ready to verify?

Open the canvas and simulate these parameters in real-time.

Node_Architecture

inputSource CopyBatch Mode
llm_callFast Translatorgemini-1.5-flash
tool_callFormat QA ToolSchema Validation
routerQuality RouterPass / Fail Strategy
outputDelivery (Pass)Standard
llm_callHeavy Translatorclaude-3-7-sonnet
tool_callHeavy QA ToolSchema Validation
outputDelivery (Fail)Fallback

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