implementation

Implement Data Generation Workflow with DSPy

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Develop a Low-Resource Language Data Generation Crew

Format

Text-first

Lines

Sections

Linked challenge

Develop a Low-Resource Language Data Generation Crew

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Implement the core data generation and initial validation workflow using CrewAI. Focus on how the 'Data Generator' agent uses Gemini 2.5 Pro for content creation and how DSPy is applied to optimize its prompts for output quality in the low-resource language.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

Variables

Lists

Code blocks

Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Develop a Low-Resource Language Data Generation Crew

Challenge of building LLMs for low-resource languages, this challenge focuses on creating an intelligent agent crew to significantly accelerate and enhance the creation of high-quality, culturally-relevant datasets. The lack of robust training data is a major hurdle for these languages, and traditional methods are slow and costly. Your task is to build a multi-agent system that collaborates to generate synthetic text, translate, and perform linguistic and cultural validation for a specified low-resource language. The system will employ Gemini 3 for advanced generative capabilities and cross-lingual understanding, alongside specialized agents for validation. Crucially, the agents will communicate via an A2A Protocol, ensuring efficient hand-offs and feedback loops, with human-in-the-loop checkpoints for final verification.

Open challenge

Related prompts

Browse library

CrewAI Agent & Task Definition

planning

A2A Communication Protocol Design

planning

Human-in-the-Loop Integration

implementation