As AI systems become more integral to business processes, automated document understanding is emerging as a powerful tool for increasing productivity and effectiveness. As we discussed in parts one, two, and three of our Overcoming the Limitations of LLMs series, it is not enough to rely on large language models (LLMs) for complex document understanding. Much more is needed.
Document understanding requires a sophisticated approach that goes beyond the use of LLMs alone. That starts with a proactive approach to quality; for example, de-skewing and de-blurring scanned documents to increase optical character recognition (OCR) accuracy. It also includes intelligent parsing, extracting, and structuring of data where appropriate. Tabular data, for example, can be difficult to work with, which is why Instabase AI Hub provides automated mechanisms for structuring, storing, and retrieving such details.
Without proper data validation, the risk of errors remains. This can lead to significant negative consequences for decision-makers who rely on accurate, timely information.
Documents come in an infinite array of formats and languages and often include complicating factors such as errors, omissions, and inconsistencies. Correctly parsing information, structuring it, and interpreting contextual nuances is an extraordinarily complex undertaking for automated systems.
Human augmentation of AI systems through a systematic review of AI outputs is critical to ensure these systems deliver the desired results. However, the key to balancing the benefit of automation with the requirement for accuracy is knowing when to loop in a human and then minimizing the verification effort. Data validations provide a measure of confidence that the extracted data is accurate and contextually appropriate. They allow us to determine which data can be automatically processed and sent downstream and which require the attention of a human operator.
This process enables automation or “straight-through processing” and helps maintain desired levels of accuracy. Human review is essential to this process, providing a layer of scrutiny that machines alone cannot achieve. Humans can assess subtleties and ambiguities often present in complex documents, ensuring the final output is accurate and meaningful.
Despite significant advancements in AI technology, LLM responses still require validation and human review to safely run in a production setting. This calls for a broader system built around LLMs that includes automated validations, human review interfaces, and the orchestration of teams and workflows. Such systems are critical in many use cases and industries where accuracy is paramount to prevent errors that could have very far-reaching implications.
Understanding and Implementing Data Validation Rules
This article specifically explores data validation; that is, confirming the accuracy of responses to the greatest degree possible. Instabase AI Hub uses multiple techniques to accomplish this:
- Confidence scores are a probabilistic metric used to score the likelihood of accuracy for any given response. They are based on a combination of factors, including OCR confidence, prompting, and log probabilities. They serve as the primary trigger for human review.
- Validation logic refers to rules that check the accuracy of an output based on our understanding of the expected response. For example, a zip code should come in a five-digit numerical format and the total of an invoice should equal the sum of the subtotal and tax.
- Business rules are validations that align with internal policies and procedures and support automation beyond just data extraction. For example, business rules can be used to confirm whether or not a driver’s license is expired or flag financial transactions over a certain threshold, reducing risk and helping operational teams work more efficiently.
- External system validation enables automated checks against external data sources to determine the accuracy of data provided by AI Hub. For example, if your use case involves responses that contain customer information, that data can be matched against detailed records in your customer relationship management (CRM) or enterprise resource planning (ERP) system. With AI Hub, you can add custom code to query those systems, confirming that the customer exists and that information provided in the response is accurate.
AI Hub includes a prompt-based mechanism for defining validation rules and generating deterministic logic and test cases from simple prompts. This makes it easy for anyone, regardless of technical expertise, to create robust document understanding solutions.
Facilitating and Streamlining Human Review
No AI system is 100% accurate. Human review is an essential element for producing high-quality outcomes. That’s why Instabase has made it integral to AI Hub, offering advanced features and configurability that enable administrators to fine-tune their human review process.
AI Hub’s Human Review Suite lets you efficiently manage groups of reviewers, assign review and escalation tasks, and monitor and optimize review performance. The interface displays extracted data alongside the source document and highlights the exact words for fast referencing across documents of any length. It incorporates features that maximize reviewer efficiency, such as multi-monitor setups, keyboard shortcuts, and auto-suggestions to make corrections fast and accurate.
The Human Review Suite is built for scale, allowing operations teams in enterprises to define and manage different review queues, with various escalation paths based on document types and other factors.
By combining validations with human review using AI Hub’s purpose-built tools and workflow engine, organizations can optimize and fine-tune the efficiency and accuracy of their document understanding processes.
Automate, Validate, and Review With Instabase AI Hub
Instabase AI Hub provides a turnkey solution for automating document understanding, leveraging AI to boost productivity and data validation and human review to ensure accuracy.
Document processing is inherently complex due to the huge diversity of formats, various languages, and document quality. AI Hub addresses these challenges by integrating proactive quality measures and employing intelligent parsing and structuring techniques. It enhances document understanding by combining validation checks, including confidence scores, validation logic, and external system validation to confirm data accuracy.
Despite AI’s advanced capabilities, human review remains essential for managing subtleties and ambiguities and ensuring the correct data is extracted accurately. Confidence scores help trigger human review when AI results fall below a certain threshold, while custom validations provide additional integrity checks, including the ability to verify against external data sources. AI Hub makes data validation fast, efficient, and simple via the Human Review Suite.
Instabase AI Hub is designed to unlock the full potential of LLMs, so you can achieve accurate, deeply contextual document understanding. Our platform gives you everything you need to turn documents, images, emails, and other unstructured data into powerful insights and better decisions with generative AI.
Download our whitepaper “LLMs Are Not All You Need: Full Stack Document Understanding with Instabase AI Hub” to dive deeper into this topic.
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