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.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
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 1 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Create a test suite that simulates a stream of diverse prompts, including those with mixed sentiment and specific brand mentions. Verify that the `PromptMonitor` correctly ingests them, the `BrandAnalyzer` accurately applies DeepSeek R1 for sentiment, and the `ReportGenerator` produces coherent summaries. Pay close attention to the handover of information between agents. Use Blaxel for observing agent interactions and potential bottlenecks. Provide examples of expected intermediate agent outputs at each stage.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Agents for Prompt-Driven Brand Sentiment & Affinity
This challenge focuses on building a sophisticated multi-agent system using CrewAI to analyze brand mentions and sentiment within a stream of AI-generated prompts. The system will orchestrate specialized agents to monitor, categorize, and report on brand affinity. The core idea is to simulate an intelligent monitoring platform that provides actionable insights into brand perception and recommendation patterns from diverse data sources, leveraging CrewAI's role-playing architecture. Developers will design a collaborative agent team, where each agent has a distinct role, tools, and goals. For instance, a 'Prompt Ingestor' agent, a 'Brand Analyst' agent, and a 'Report Generator' agent will work in concert. The system will use DeepSeek R1 for its advanced reasoning capabilities to accurately interpret nuanced sentiment and complex brand associations. Integration with Zapier Interfaces will enable seamless data ingestion from various sources, while tool can facilitate custom workflow automation for alert triggers and data processing. Voiceflow will provide a conversational interface for real-time query and status updates, making the system highly interactive. Blaxel will serve as the underlying agent orchestration backbone, ensuring robust agent management.
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.