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Overview

Prompt chaining is an AI workflow pattern that decomposes a complex task into a sequence of steps, where each LLM call processes the output of the previous one. This approach trades off latency for higher accuracy by making each LLM call an easier, more focused task, with the ability to add programmatic checks between steps to ensure the process remains on track. Generating and translating copy

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Example task

In this example, we’ll create a workflow that generates and translates copy. This approach is particularly effective when tasks require different models or approaches for different inputs. This task:
  • Uses generateText from Vercel’s AI SDK to interact with OpenAI models
  • Uses experimental_telemetry to provide LLM logs
  • Generates marketing copy based on subject and target word count
  • Validates the generated copy meets word count requirements (±10 words)
  • Translates the validated copy to the target language while preserving tone
import { openai } from "@ai-sdk/openai";
import { task } from "@trigger.dev/sdk";
import { generateText } from "ai";

export interface TranslatePayload {
  marketingSubject: string;
  targetLanguage: string;
  targetWordCount: number;
}

export const generateAndTranslateTask = task({
  id: "generate-and-translate-copy",
  maxDuration: 300, // Stop executing after 5 mins of compute
  run: async (payload: TranslatePayload) => {
    // Step 1: Generate marketing copy
    const generatedCopy = await generateText({
      model: openai("o1-mini"),
      messages: [
        {
          role: "system",
          content: "You are an expert copywriter.",
        },
        {
          role: "user",
          content: `Generate as close as possible to ${payload.targetWordCount} words of compelling marketing copy for ${payload.marketingSubject}`,
        },
      ],
      experimental_telemetry: {
        isEnabled: true,
        functionId: "generate-and-translate-copy",
      },
    });

    // Gate: Validate the generated copy meets the word count target
    const wordCount = generatedCopy.text.split(/\s+/).length;

    if (
      wordCount < payload.targetWordCount - 10 ||
      wordCount > payload.targetWordCount + 10
    ) {
      throw new Error(
        `Generated copy length (${wordCount} words) is outside acceptable range of ${
          payload.targetWordCount - 10
        }-${payload.targetWordCount + 10} words`
      );
    }

    // Step 2: Translate to target language
    const translatedCopy = await generateText({
      model: openai("o1-mini"),
      messages: [
        {
          role: "system",
          content: `You are an expert translator specializing in marketing content translation into ${payload.targetLanguage}.`,
        },
        {
          role: "user",
          content: `Translate the following marketing copy to ${payload.targetLanguage}, maintaining the same tone and marketing impact:\n\n${generatedCopy.text}`,
        },
      ],
      experimental_telemetry: {
        isEnabled: true,
        functionId: "generate-and-translate-copy",
      },
    });

    return {
      englishCopy: generatedCopy,
      translatedCopy,
    };
  },
});

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Run a test

On the Test page in the dashboard, select the generate-and-translate-copy task and include a payload like the following: 
{
  marketingSubject: "The controversial new Jaguar electric concept car",
  targetLanguage: "Spanish",
  targetWordCount: 100,
}
This example payload generates copy and then translates it using sequential LLM calls. The translation only begins after the generated copy has been validated against the word count requirements.
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