This blog explores how legal and other professionals can effectively manage AI data challenges to enhance productivity and accuracy.

The Dynamics of Data Overload in AI Systems

As organizations harness the power of AI to enhance productivity and decision-making, the volume of data accessible to these systems can be both an asset and a challenge. When AI systems, such as Microsoft 365 Copilot, leverage Retrieval-Augmented Generation (RAG) methods to access vast amounts of enterprise and web data, the potential for issues like the “too much data” problem becomes evident.

RAG systems like Copilot 365 are designed to retrieve relevant information from a wide range of sources and generate contextually appropriate responses. However, exposing an AI model to vast amounts of information can make it difficult to filter out what is essential versus what is irrelevant, especially when different versions of documents or outdated copies of the same information are present.

The Challenge of Too Much Data in RAG Systems 

1) Understanding the Lost in the Middle Phenomenon

The 'Lost in the Middle' phenomenon is a well-documented issue where AI models tend to focus on the initial and final portions of the information provided while neglecting the content in the middle. For legal professionals relying on precise and nuanced information, this can lead to incomplete or skewed responses.

For example, if a model is asked to provide a list of specific steps required for a legal procedure, it may end up focusing only on the first and last steps, missing critical steps in between. This can result in an incomplete or even incorrect procedural guide, which is problematic in legal contexts where every detail matters.

2) Navigating Mixed Data Sources in Legal Contexts

The default settings in AI systems often pull data from multiple, potentially irrelevant sources, including information from the web or even outdated content that the model was originally trained on. This can introduce inaccuracies when responding to complex queries.

For instance, when answering a legal question, the model might mix trusted internal documents with less reliable information from general web sources or outdated public data. This can lead to a response that lacks the precision and authority needed in a legal context, thereby reducing the reliability of the information provided.

3) Leveraging Semantic Indexing and Reranking for Better AI Performance

Microsoft 365 Copilot incorporates semantic indexing and reranking mechanisms to improve the quality of retrieved content. Semantic indexing helps create a structured index that organizes information by meaning and relevance, ensuring that important documents like recent case law or relevant statutes are more easily found.

Reranking prioritizes sources based on their relevance to the user’s query, helping to filter out noise and surface the most pertinent data. However, even with these solutions, limitations exist. The AI may still pull information from a large set of data that includes tangentially relevant documents, leading to mixed results.

The Future of Legal Tech AI with Atlas Intelligent Knowledge Studio

Platforms like Atlas IKS address these challenges by allowing users to create and manage authoritative knowledge collections that streamline AI retrieval processes. Instead of sifting through thousands of potentially unrelated documents, Atlas IKS focuses on well-curated collections to provide responses that are accurate and contextually aligned with the user’s needs. 

Think of Atlas IKS as a curated bookshelf in a lawyer’s office, where only the most relevant case books and legal texts are available. When Copilot uses this curated collection, it quickly finds and references the most authoritative sources, leading to better, more reliable answers. 

While AI models like Copilot 365 are incredibly powerful, the “too much data” problem poses a significant challenge, especially for industries that rely on precise and contextual information, such as the legal field. Although semantic indexing and reranking help mitigate these challenges, curated platforms like Atlas IKS provide a more focused approach, ensuring that AI outputs are reliable and contextually relevant. 

By understanding these challenges and implementing best practices for prompt engineering and data management, enterprises can harness the full potential of AI while minimizing the risks associated with data overload. 

Why It Matters: A Legal AI Use Case 

Imagine a law firm using Copilot 365 to assist with legal research and case summaries. The firm has an extensive digital library that includes: 

• Case law archives 

• Client contracts 

• Regulatory guidelines 

• Internal memos and notes 

When a lawyer asks Copilot, “Summarize the latest updates in European intellectual property law,” the AI needs to sift through hundreds of documents, including recent case law, policy updates, internal memos, and archived legal opinions, to provide an answer. Here’s where the “too much data” problem becomes apparent: 

• Redundant Retrieval: The AI pulls data from irrelevant memos, outdated regulations, or even drafts that are no longer applicable, diluting the quality of the response. For instance, it might pull outdated regulations that have already been repealed or superseded by newer case law, leading to a response that includes conflicting or irrelevant information, thus reducing reliability. 

• Focus Shift: Due to the lost in the middle phenomenon, the AI might only consider the initial section of an authoritative document while also incorporating conclusions from unrelated internal notes, ignoring critical points in the middle. For example, if the latest intellectual property law includes detailed amendments in the middle of the document that significantly change the interpretation of certain clauses, the AI might miss these entirely. As a result, the summary provided to the lawyer could be incomplete or incorrect, overlooking crucial changes that are pivotal to understanding the law in its current form. 
  
  

Mitigation Strategies: How Copilot 365, Semantic Indexing, and Atlas Can Help 

Microsoft 365 Copilot incorporates semantic indexing and reranking mechanisms to improve the quality of retrieved content. Here’s how these features work: 

1. Semantic Indexing: This feature helps create a structured index that organizes information by meaning and relevance. In legal use cases, semantic indexing ensures that important documents like recent case law or relevant statutes are more easily found.
2. Reranking: The AI prioritizes sources based on their relevance to the user’s query, helping to filter out noise and surface the most pertinent data. 
3. Structured Knowledge with Atlas: Atlas helps keep your knowledge structured, tagged, and relevant, which supports the effectiveness of the previous techniques and enhances Copilot's ability to produce relevant outcomes. 

However, while these solutions are powerful, they are not without limitations. Limitations of Reranking mean that even with reranking, the AI may still pull information from a large set of data that includes tangentially relevant documents, leading to mixed results. Additionally, Complexity in Legal Contexts arises because legal queries often require a depth of understanding and nuanced interpretations that are difficult for AI systems to achieve when too much irrelevant data competes for attention. 

Even if Atlas provides structure to your knowledge, a significant effort is still needed to clean up and organize all your existing Microsoft 365 content. This process may not be as effective as expected for improving Copilot outcomes, given that access to users' inboxes and OneDrive files remains, which are spaces where the Atlas knowledge governance structure cannot reach.