I ventured into the intriguing realm of General AI (Gen IA) prompted by business needs, stemming from my background in traditional AI, particularly in vision AI. The task at hand involves developing a bot for Intelligent Document Processing (IDP), which I initially embarked upon. Let’s begin from the outset.
Understanding Intelligent Document Processing (IDP): Intelligent Document Processing (IDP) involves leveraging advanced technologies, including artificial intelligence (AI) and machine learning (ML), to automate document processing within businesses or organizations. The primary aim of IDP is to streamline and enhance document-related workflows by extracting valuable information from various document types, such as invoices, receipts, contracts, forms, and more.
Understanding Generative AI: Generative AI encompasses features within AI that create original content. Typically, people interact with generative AI integrated into chat apps. Generative AI applications receive natural language inputs and return appropriate responses in various formats, including natural language, image, code, and audio.
General Steps in Implementing Intelligent Document Processing (IDP):
- Define Objectives and Scope: Clearly define the objectives and scope of your IDP implementation, including the types of documents to process and specific information extraction requirements.
- Select Suitable Technology: Choose the appropriate IDP technology or solution based on your requirements. Consider factors such as document types, volume, and complexity of information extraction.
- Data Collection: Gather a diverse set of training data representing the range of documents your system will handle. Annotate the training data to provide labeled examples for learning.
- Preprocessing: Clean and preprocess documents to enhance information extraction accuracy. Normalize the format and structure of documents.
- Train the Model: Utilize machine learning algorithms, such as natural language processing (NLP) and machine vision, to train your IDP model on the annotated dataset.
- Integration: Integrate the trained IDP model into existing systems or workflows, ensuring seamless communication.
- Testing and Validation: Conduct thorough testing to ensure accuracy and reliability. Validate the system with a diverse set of documents in real-world scenarios.
- Optimization: Fine-tune the model based on testing performance. Adjust parameters and features to improve accuracy and efficiency.
- Deployment: Deploy the IDP system to production after successful testing and optimization. Monitor closely during the initial deployment phase.
- Continuous Improvement: Implement mechanisms for continuous monitoring and improvement based on user feedback.
- Security and Compliance: Ensure IDP implementation adheres to security and compliance standards, protecting sensitive information.
- User Training: Train end-users on interacting with the IDP system, providing documentation and support.
We will utilize some of these steps tailored to our specific problem. In AWS, we can address challenges through a combination of services or by utilizing BedRock. Adhering to the AWS Well-Architected Framework, we will also assess cost implications for Sao Paulo AZ. Further details on the RAG technique and its application will be discussed in the next part of this article.