Overview
HR departments face mounting pressure to optimize processes while managing increasing workloads. Tasks like drafting emails and maintaining policy documents consume significant resources. Our solution combines AI-driven content generation with Future AGI's evaluation suite to ensure quality and compliance. The system includes comprehensive checks for bias, cultural sensitivity, accuracy, and data privacy. Using a single prompt, our tool processes various HR data to generate appropriate communications. This automation allows HR teams to focus on strategic work while maintaining high standards and improving employee experience.
Problem Statement: The Challenges of Manual HR Management
HR departments face key challenges that limit their efficiency. The high volume of data and communications creates bottlenecks and over-reliance on manual work. These challenges include:
Time-Consuming Tasks: Routine activities like drafting emails, updating policies, and reviewing data consume valuable time that could be spent on strategic work.
Inconsistent Communication: Manual content creation leads to variations in tone and messaging, causing confusion and requiring frequent corrections.
Human Error: Manual processing increases risks of mistakes in documents, emails, and reviews, creating additional work to fix errors.
Compliance Issues: Manual review struggles to ensure communications meet all regulatory requirements, raising risks of non-compliance.
Poor Scalability: Traditional methods cannot effectively handle growing workloads as organizations expand.
Inflexible Data Structure: Current systems use rigid formats that limit data processing and workflow optimization.
These challenges demonstrate the need for automation that maintains quality while reducing manual effort. The current approach leads to inefficiency and staff burnout.
To demonstrate the problems with existing HR data and systems, we have utilized two different types of datasets:
Dataset 1: Existing Knowledge Base:
The first dataset represents a typical internal knowledge base, full of messages, announcements and training material. This is used to demonstrate the common issues with existing HR data, such as bias, lack of cultural sensitivity, and inaccurate content. This existing data is used to showcase the challenges that HR must navigate on a daily basis and the importance of a powerful evaluation suite.
Columns Description:
ID: Unique identifier for each document or communication
Category: Type of document (e.g., Policy, Training, Announcement, Email)
Language: Primary language of the document
Text: Actual content of the document or communication
This flexible data structure allows for easy processing of various document types while maintaining essential metadata. The simple column structure makes it easier to generate different types of communications from the same dataset.
Dataset 2: Flexible Data for Generation:
The second dataset provides data for the generation feature, it provides the raw data that will be used to generate content using our AI system. This data is designed to showcase how diverse data points can be used to create emails, documents and communications. This data was used to highlight the shortcomings of existing structured data and showcase the strengths of using a more flexible data structure, and show that data does not have to be highly curated before being used for specific tasks.
Columns Description:
ID: Unique identifier for each data entry to track and reference specific records
Category: Classification of the content type (e.g., Employee Onboarding, Performance Review, Training Material, Benefits)
Details: Comprehensive information about the specific event, policy, or communication including relevant dates, participants, and requirements
Key Points: Essential highlights or action items that need to be communicated or addressed in the generated content
This structured approach allows our system to process diverse HR data efficiently while maintaining context and ensuring all crucial information is captured for accurate content generation.
Our Solution
Evaluating Existing Internal Knowledge Base
Our solution begins with a powerful analysis of an organization's existing internal knowledge base, including documents, messages, and announcements. We understand that seemingly benign internal communications can harbor subtle issues such as bias, lack of cultural sensitivity, factual inaccuracies, inappropriate content, and data privacy concerns. Our suite of advanced evaluations is designed to thoroughly assess existing content and identify areas for improvement. Future AGI's evaluation system includes a diverse set of checks that include:
Bias Detection: Identifies and flags instances of gender, racial, cultural, or ideological bias, promoting balanced perspectives and neutral language.
Cultural Sensitivity: Checks for cultural appropriateness, inclusive language, and awareness of cultural nuances, preventing potentially offensive content.
As we can see here some of the internal documents have failed the cultural sensitivity and bias tests, which might be not caught up by a human considering the large volume of data being processed regularly.
Factual Accuracy: Verifies if the content is factually correct, enhancing reliability and preventing misinformation.
Thus factual accuracy becomes one of the important metrics to evaluate on, as it prevents spreading of misinformation.
Sexist Content: Specifically detects and flags sexist language and gender bias.
Content Moderation: Assesses for safety using OpenAI’s content moderation tool, identifying harmful or inappropriate content.
Data Privacy Compliance: Checks outputs for compliance with regulations like GDPR and HIPAA, identifying potential privacy violations.
Safe For Work Text: Assesses text for its suitability in a professional setting, flagging inappropriate language or references.
By automatically applying these evaluations, our system provides HR teams with a comprehensive understanding of the strengths and weaknesses of their current internal communications, allowing them to identify potential risks and make necessary changes to improve both the quality and the consistency of all forms of HR communication.
Generating and Evaluating New Documents
Setting Up the Experiment
Building on the insights gained from our analysis of existing data, our solution also includes AI-powered prompt-based generation for creating new HR documents and communications using the Experiment feature. This innovative feature addresses common issues with HR data, especially its inflexibility, by generating communications directly from the provided data. The system uses a single prompt and the selected data column and can generates emails, policy documents, training invitations, employee feedback, and job descriptions by using a single prompt with placeholders for different variables.
The prompt uses three key variables - category, details, and key points - to generate appropriate HR communications while maintaining consistency and professionalism across different document types.
The experiment feature shown in the image offers various configurations, including model selection and the ability to add multiple models for output generation. Users can add multiple prompts through the "add message" button and configure model hyperparameters. The interface also provides direct access to Future AGI's evaluation features.
This flexibility allows HR to focus on using data to generate communication instead of curating and adapting data for different use cases. By using a single prompt and the identified category, our system is able to automatically interpret the requirements of the provided data.
In this usecase, we employed two different large language models (LLMs) for generating content: GPT-4o and Claude 3.5 Sonnet. This approach allows us to produce outputs with different styles and tones.
Analyzing Individual Outputs
The outputs from both models undergo rigorous assessment through Future AGI's Evaluations, which ensure accuracy, inclusivity, compliance, and workplace appropriateness. The evaluation system checks all generated content for bias, cultural sensitivity, factual accuracy, content moderation, data privacy, and proper URL and email formatting—selecting the best output based on these criteria. This comprehensive approach ensures that our system not only streamlines HR tasks but also upholds the highest standards of communication and data integrity. Our system provides a complete lifecycle approach: first analyzing the data, then using it to create content, and finally evaluating the outputs through our assessment tools. This provides flexibility in both content generation and output quality.
Summary of our Experiment
Our solution provides a comprehensive summary page that provides a detailed overview of the evaluation results for outputs from different LLMs. This summary interface consolidates all evaluation metrics, making it simple to compare and contrast the performance of different language models across various criteria such as accuracy, bias detection, cultural sensitivity, and content appropriateness. This feature enables HR teams to make data-driven decisions about which model outputs best suit their specific communication needs and organizational standards.
Choosing a Winner
The "Choose Winner" button provides a powerful functionality that allows us to rank and select the most suitable LLM outputs based on our specific requirements and evaluation criteria. This feature enables HR teams to systematically compare different model outputs and select the one that best aligns with their communication goals, compliance standards, and organizational values. By incorporating this ranking system, we ensure that only the highest quality, most appropriate content is selected for use in HR communications.
Based on the evaluation results shown above, Claude-3.5 Sonnet emerged as the clear winner.
Key Results
Our integrated solution, combining AI-powered prompt-based generation with Future AGI evaluations, delivers significant improvements across key HR functions:
Increased Efficiency: We've seen a 65% reduction in time spent on routine document creation, allowing HR teams to focus on strategic priorities.
Improved Communication Quality: Our system achieves a 90% reduction in errors in HR communications, thanks to Future AGI evaluations and the selection of the best output from our dual LLM generation.
Reduced Workload: Manual effort for document creation and communication tasks is reduced by 75%, freeing up valuable HR resources.
Enhanced Compliance: Future AGI evaluations ensure a 99% compliance rate with regulations and policies across all generated communications, minimizing the risk of legal and regulatory issues.
Scalable Operations: Our system maintains a consistent throughput regardless of data volume, showcasing its ability to scale with any organization.
Conclusion
Our integrated solution, combining AI-powered prompt-based generation with Future AGI evaluations, offers a transformative approach to HR management. We’ve shown how a single prompt can generate diverse communications while adhering to organizational communication guidelines. By also using two different LLMs to showcase different styles of communication, we've also shown the versatility of our system when producing content that is appropriate for specific use cases. Our system significantly reduces the burden of manual work, providing HR teams with a much more efficient and reliable way to manage their data and communications.
Future AGI evaluations provide a robust safety net for your HR data and communications, identifying problems that might otherwise go unnoticed, and guaranteeing high standards for accuracy, inclusivity and compliance. By proactively analyzing existing data and assessing newly generated content, we empower HR professionals to maintain control and consistently manage standards in their day to day operations. Our system also tackles the problem of rigid data formats, by creating a system that can be used to create outputs from flexible and unstructured data. This allows HR to not only maintain high standards but also to significantly boost productivity.
