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.
On the backend (an AI SDK API route), integrate Unstructured.io to parse incoming FOIA request documents (e.g., PDF or plain text). Write a TypeScript function `parseDocument(fileBuffer: Buffer): Promise<string>` that takes the raw file buffer, sends it to Unstructured.io, and returns the clean, extracted text content. Handle common parsing errors.
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.
Automate FOIA Request Processing
This challenge requires designing and implementing an intelligent automation system to efficiently process and filter high volumes of incoming Freedom of Information Act (FOIA) requests. Leveraging Vercel's AI SDK, participants will build a responsive web interface for request submission and monitoring. OpenAI o4-mini will serve as the core AI engine for document classification, summarization, and anomaly detection to identify spam or 'robo-requests'. Key aspects include integrating Unstructured.io for robust document parsing, orchestrating the multi-step workflow with Lindy for compliance and auditing, and setting up Langsmith for comprehensive observability of AI agent decisions and human review queues. The solution aims to streamline administrative tasks, reduce human workload, and ensure that legitimate FOIA requests are processed accurately and efficiently.
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.