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.
Identify a specific module within your data center optimization project where code generation could significantly accelerate development (e.g., a function for calculating total energy cost given specific hardware specs, a script for simulating lead times from different vendors). Write a detailed prompt for StarCoder 2 that would generate this code. Integrate the generated code into your overall solution and demonstrate its functionality.
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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Smart Data Center Hardware Procurement & Energy Optimization with StarCoder 2 and Seldon Core
The 'picks and shovels' of today's data center gold rush involve managing massive, rapidly evolving hardware infrastructure (GPUs, ASICs, networking gear) and optimizing their energy consumption. This challenge tasks developers with creating a system that intelligently forecasts demand for specific data center hardware components, optimizes procurement strategies amidst volatile supply chains, and models energy efficiency to reduce operational costs and environmental impact. Participants will build a predictive and optimization platform, utilizing advanced code generation for simulation and model deployment tools. The solution should enable data center operators to make informed decisions regarding hardware acquisition, deployment, and energy management, addressing the growing needs and complexities of critical digital infrastructure.
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.