Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
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Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 20 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop a simple interactive prompt (simulating a Bito AI chat interface) where a user can ask the `MarketMonitorAgent` about recent trends. The agent should:
1. Use `searchTrendsInPinecone` to retrieve relevant historical trends based on the user's query.
2. Synthesize information from `Mistral Large 2` and Pinecone to provide a coherent and informative response.
3. Focus on providing actionable insights or explanations, not just raw data. This will be evaluated by the `UserQueryResponse` task.
```typescript
// ... (previous MarketMonitorAgent setup)
async function handleUserQuery(query: string) {
console.log(`User query: ${query}`);
const response = await MarketMonitorAgent.run({
prompt: `Respond to the user's query about market trends: "${query}". Use the searchTrendsInPinecone tool to gather historical context and provide a comprehensive explanation.`,
context: {
userQuery: query
}
});
console.log('MarketMonitorAgent:', response.response); // Assuming response contains the LLM's final answer
}
// Simulate user interaction for evaluation
// handleUserQuery('What is the current trend for X daily active users?');
// handleUserQuery('Tell me about recent shifts in Threads engagement.');
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Real-time Market Trend Agent
Develop a real-time market trend analysis agent using Mastra AI, designed to monitor specific metrics (e.g., user engagement on social platforms, e-commerce order volumes) and identify significant shifts. The agent will leverage Mistral Large 2 via the Hugging Face Inference API for advanced pattern recognition and sentiment analysis. It will utilize Pinecone as a vector store to maintain a historical context of trends and associated data points, preventing information overload by focusing on novel insights. A Bito AI-like interface component will allow users to interact with the agent for on-demand reports and trend explanations, requiring efficient, scalable processing to deliver timely insights.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.