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 Haystack-based RAG pipeline utilizing OpenAI o3 via Fireworks AI to analyze simulated energy news articles. Your pipeline should extract real-time market sentiment (e.g., 'bullish', 'bearish', 'neutral') or specific market signals (e.g., 'supply increase', 'demand decrease') relevant to BESS operations. Describe your prompt engineering strategies and demonstrate the accuracy of your sentiment extraction.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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 BESS Arbitrage with LLM-Enhanced Market Forecasting
The volatile nature of energy markets, as exemplified by the ERCOT BESS revenue 'roller coaster,' presents both challenges and opportunities for Battery Energy Storage Systems (BESS) operators. With falling battery prices and increasing global installations, optimizing BESS dispatch for arbitrage is crucial. This challenge tasks developers with building an AI-driven system that maximizes BESS revenue by making intelligent charge/discharge decisions in a simulated real-time market environment. The system will leverage advanced time-series forecasting for energy prices and integrate market sentiment analysis derived from real-time news and reports using cutting-edge Large Language Models (LLMs). Participants will develop a robust forecasting model that considers various market factors, including historical prices, weather data, and demand forecasts. A key innovative component will be the integration of an LLM-powered pipeline to extract actionable sentiment from energy news, influencing price predictions and optimization strategies. The final solution should dynamically adjust BESS operations to exploit price differentials, balancing profitability with battery degradation costs, and demonstrate superior performance against a baseline strategy in a simulated market.
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.