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.
Detail the steps and provide example code for setting up the RAG system that your Mastra AI agent will use. Describe how you would ingest diverse enterprise data (e.g., HR policies, financial reports, operational manuals) into a vector database (like Pinecone or Chroma), generate embeddings, and ensure efficient retrieval. Show how the `rag_retriever` tool would interface with this vector database to fetch contextually relevant information for the agent's analysis.
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.
AI-Powered Productivity Agent for Enterprise Cost Optimization
Design and implement a Mastra AI agent system to address the challenge of boosting enterprise productivity and optimizing costs. This system will leverage RAG with internal company data and external industry reports to identify inefficiencies, suggest process improvements, and automate routine analytical tasks. The core challenge is to build a scalable and intelligent agent that can ingest diverse data, perform complex analysis, and recommend actionable strategies, working in concert with other automation platforms like Lyzr.
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.