What is PromptReports.AI?

PromptReports.AI is an AI-powered platform that creates professional executive research reports in minutes. It provides market analysis, financial reports, and strategic intelligence customized for your business needs.

How does AI report generation work?

Our platform uses advanced AI models to analyze your input data and generate comprehensive research reports. Simply provide your topic or data, select a report template, and our AI creates a professional document with insights, analysis, and recommendations.

What types of reports can I create?

You can create market analysis reports, competitive intelligence reports, trend scanning reports, financial analysis, strategic assessments, and custom research reports tailored to your specific industry or topic.

Yes, we prioritize data security. All data is encrypted in transit and at rest. We do not share your data with third parties, and you maintain full ownership of all reports generated on our platform.

Annotation Queues

Human-in-the-loop validation: Review and improve AI outputs with expert feedback to ensure quality and accuracy.

What are Annotation Queues?#

Annotation Queues enable systematic human review of AI-generated content. Create queues to collect expert feedback, validate outputs, and continuously improve the quality of your AI-generated reports. This human-in-the-loop approach combines the speed of AI with the judgment of human experts.

Quality Assurance

Ensure AI outputs meet your quality standards before publication.

Expert Review

Route content to subject matter experts for validation.

Feedback Collection

Gather structured feedback to improve prompt performance.

Continuous Improvement

Use annotations to refine prompts and models over time.

Enterprise Feature

Annotation queues are particularly valuable for enterprise deployments where accuracy and compliance are critical. They provide an audit trail of human review for regulated industries.

Human-in-the-Loop#

Human-in-the-loop (HITL) is a design pattern where AI and humans work together. The AI handles the heavy lifting of content generation, while humans provide oversight, validation, and correction where needed.

Component	AI Role	Human Role
Content Generation	Creates initial draft quickly	Reviews for accuracy and tone
Quality Assessment	Flags potential issues	Makes final judgment calls
Domain Knowledge	Applies general knowledge	Adds specialized expertise
Improvement	Learns from feedback patterns	Provides specific corrections

Creating Queues#

Create annotation queues to organize your review workflow:

Name Your Queue

Choose a descriptive name like "Report Quality Review" or "Technical Accuracy Check".

Add Description

Explain the purpose and review criteria for this queue.

Create Queue

Click create to set up the queue. It will be active immediately.

Add Tasks

Route AI outputs to the queue for review. Tasks can be added automatically or manually.

Queue Organization

Create separate queues for different types of reviews: content accuracy, brand voice, technical validation, etc. This helps route tasks to the right reviewers.

Managing Tasks#

Each item in a queue is a task that needs human review. Tasks contain the AI output along with context about how it was generated.

Task Element	Description
Content	The AI-generated output to review
Prompt	The prompt that generated the content
Model	Which AI model was used
Status	Pending, In Progress, Approved, or Rejected
Annotations	Feedback and corrections from reviewers
Timestamp	When the task was created

Task Queue

View all pending tasks and their current status.

Rating System

Score content quality on multiple dimensions.

Approval Flow

Approve or reject content with required justification.

Batch Operations

Process multiple tasks efficiently with bulk actions.

Annotation Workflow#

A typical annotation workflow follows these steps:

Content Generation

AI generates content using your prompts and templates.

Automatic Routing

Content meeting certain criteria is automatically routed to the appropriate queue.

Expert Review

Reviewers examine the content, checking for accuracy, tone, and completeness.

Annotation

Reviewers add comments, corrections, and quality ratings.

Approval/Rejection

Content is either approved for use or sent back for regeneration.

Feedback Loop

Annotations are used to improve prompts and fine-tune models.

Pro Tips#

Best Practices

Define clear review criteria and share them with all reviewers
Use multiple queues to separate different types of reviews
Set up automated routing rules based on content type or risk level
Review annotation patterns to identify systematic prompt improvements
Establish SLAs for review turnaround to keep workflows moving
Use the feedback to create a library of "golden" examples

Annotation queues transform AI content generation from a single-shot process into a continuous improvement cycle. Over time, the combination of AI efficiency and human expertise produces consistently high-quality outputs.

PreviousMemory Systems

NextContexts