AI-Powered Regulatory Drafting Assistant
The US Department of Transportation's use of Gemini to draft federal regulations highlights the potential for generative AI to revolutionize highly structured, text-heavy workflows. This challenge focuses on building an interactive, AI-powered assistant designed to accelerate the drafting of regulatory documents. You will leverage the Vercel AI SDK to create a streaming, real-time user interface, enabling collaborative drafting and incorporating structured feedback mechanisms. The core AI will use Gemini 2.5 Pro for its advanced reasoning and text generation capabilities, specifically focusing on adherence to legal templates and factual accuracy. The solution will integrate tools for validating structured output and providing real-time quality assurance, ensuring compliance and precision in the generated text. This system will not just generate text but act as an intelligent co-pilot, guiding users through complex drafting processes while maintaining a high standard of legal and factual integrity.
What you are building
The core problem, expected build, and operating context for this challenge.
The US Department of Transportation's use of Gemini to draft federal regulations highlights the potential for generative AI to revolutionize highly structured, text-heavy workflows. This challenge focuses on building an interactive, AI-powered assistant designed to accelerate the drafting of regulatory documents. You will leverage the Vercel AI SDK to create a streaming, real-time user interface, enabling collaborative drafting and incorporating structured feedback mechanisms. The core AI will use Gemini 2.5 Pro for its advanced reasoning and text generation capabilities, specifically focusing on adherence to legal templates and factual accuracy. The solution will integrate tools for validating structured output and providing real-time quality assurance, ensuring compliance and precision in the generated text. This system will not just generate text but act as an intelligent co-pilot, guiding users through complex drafting processes while maintaining a high standard of legal and factual integrity.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
SchemaCompliance
Generated text must adhere to the specified JSON schema (validated by Ogmo AI).
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
StreamingResponse
The UI must demonstrate real-time, streaming output from the AI.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
RegulatoryAdherenceScore
Automated score based on keywords, clause structure, and factual consistency. • target: 0.85 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
InteractionResponsivenessLatency
Average time taken for the AI to respond to user input/feedback (in milliseconds). • target: 1500 • range: 0-5000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master the Vercel AI SDK for building streaming generative AI applications with React/Next.js.
Implement server-side AI model integration using Gemini 2.5 Pro via Google Cloud Vertex AI SDK for complex document generation.
Design and enforce JSON schema for structured regulatory output using Ogmo AI for validation and compliance checking.
Build a real-time feedback loop and evaluation pipeline using Larridin to monitor AI output quality and adherence to guidelines.
Develop a voice-enabled input/output interface for the drafting assistant using Bland AI for enhanced accessibility and speed.
Orchestrate tool use within the AI SDK to simulate external data lookups for factual verification during drafting.
Deploy the AI application using Vercel for scalable and performant cloud hosting.
How this agent runs
The evaluation will focus on the functionality of the AI assistant, the quality and structure of the generated regulatory text, and the real-time interactivity of the interface. Automated tests will check for schema c...
Challenge input
{ "topic": "string", "constraints": "array<string>", "desired_structure": "object" }
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ "generated_text": "string", "adherence_score": "number", "validation_errors": "array<string>" }
- Generated text must adhere to the specified JSON schema (validated by Ogmo AI).
- The UI must demonstrate real-time, streaming output from the AI.
- Automated score based on keywords, clause structure, and factual consistency. • target: 0.85 • range: 0-1
- RegulatoryAdherenceScore target: 0.85
- 2 public reference cases
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- SchemaCompliance · Weight 1
- StreamingResponse · Weight 1
- RegulatoryAdherenceScore · Weight 1
- InteractionResponsivenessLatency · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
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