Train and Augment African AI Models
Integrate rich African educational context directly into your Large Language Models (LLMs). Perfect for Retrieval-Augmented Generation (RAG) and model fine-tuning.
Request AI DatasetsRich RAG Context
Power your chatbots and AI tutors with accurate, localized educational content. Our endpoints deliver highly structured metadata (topics, difficulty, detailed explanations) perfect for vector databases.
Fine-tuning Datasets
License bulk exports of our QA pairs to fine-tune your base models on African curricula, ensuring they understand the nuances of WASSCE, JAMB, and NECO syllabi.
Model Context Protocol
Native MCP servers, allowing your AI agents to directly query our databases in real-time during user interactions, bypassing intermediate layers.
ai_rag_payload.json
{
"instruction": "Explain the concept tested in this WASSCE question.",
"input": "Solve for x: 2x + 5 = 15",
"output": "The question tests basic linear equations. To solve for x, first isolate the variable term by subtracting 5 from both sides, which gives 2x = 10. Then, divide both sides by 2 to find x = 5.",
"metadata": {
"exam": "WASSCE",
"subject": "Mathematics",
"topic": "Algebraic Equations",
"difficulty": "Easy"
}
}