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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Build a real-time dashboard that visualizes key BESS metrics, anomaly scores, and triggered alerts. Display the LLM-generated incident summaries and mitigation steps directly on the dashboard. Implement an alerting mechanism (e.g., email, Slack) that sends these LLM outputs when a critical anomaly is detected.
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 source structure until you know which part of the prompt is actually driving the result quality.
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.
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.
Predictive Anomaly Detection for BESS Thermal Runaway with AI-Driven Context
Battery Energy Storage Systems (BESS) are critical for grid stability and renewable integration, but fire safety remains a significant concern, as highlighted by recent industry testing (LSFT). This challenge focuses on building a robust, real-time anomaly detection system for BESS units to predict potential thermal runaway or other critical failures before they escalate. Developers will leverage telemetry data (temperature, voltage, current, state-of-charge, etc.) from individual battery cells and modules to identify subtle deviations from normal operating parameters. Participants will implement advanced time-series analysis and machine learning models, specifically focusing on transformer-based architectures or LSTMs, to learn complex temporal patterns and detect outliers. The solution should integrate a large language model (LLM) to provide context-aware insights and actionable recommendations when an anomaly is detected, referencing scraped safety standards and operational best practices. This ensures not only early detection but also intelligent response generation, enhancing operational safety and minimizing downtime.
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.