Addressing RAG Limitations for Large-Scale AI in Enterprises: Approach and Case-studies

Enterprises are constantly looking for ways to harness the power of artificial intelligence at a large scale. RAG offers a promising approach for LLMs to work with internal data in enterprises, but there are limitations to consider, especially for large-scale applications.

In this article, I will explain the concepts behind RAG, its limitations and how to overcome them, along with few real-world enterprise AI case-studies.

Introduction to RAG and its potential in enterprises

RAG stands for Retrieval-Augmented Generation. It’s a technique that specifically addresses the challenge of Large Language Models (LLMs) not being able to access the external data directly. RAG’s working mechanism can be summarized as a 3-step process as below:

  1. Information Retrieval: When a user submits a query, RAG first retrieves relevant information from an external knowledge base like a corporate wiki or document repository. This retrieval process relies on its own internal search mechanism.
  2. Feeding the LLM: The retrieved information is then presented to the LLM, along with the original user query.
  3. Enhanced Response Generation: With this additional context, the LLM is able to generate a more comprehensive and informative response that leverages both its own knowledge and the retrieved information.

Limitations of RAG for large-scale AI

RAG essentially augments the LLM’s capabilities by providing access to the external knowledge sources without directly granting the LLM access to the data itself. However, RAG has its limitations, specifically:

  • Scalability Issues:  Large enterprises often have massive amounts of data spread across various systems.  RAG’s retrieval process can become slow and inefficient when dealing with such vast datasets. Finding relevant information can become increasingly challenging as the data volume grows.
  • Inaccurate Chunk Similarity Search:  RAG relies on finding similar chunks of text to the query within the data. With a large data volume, the chance of noise increases. The retrieved information might not be truly relevant, leading to inaccurate or misleading outputs from the LLM.
  • Maintaining Data Relevance:  Enterprise data is constantly evolving.  Keeping the knowledge base used by RAG up to date with the latest information can be a significant challenge. Outdated information can negatively impact the quality of the retrieved data and the LLM’s responses.
  • Security Concerns:  Even though RAG doesn’t grant direct access to raw data, security vulnerabilities can still exist. Malicious actors could potentially exploit weaknesses in the retrieval system to inject misleading information or manipulate the retrieved data.

Overcoming the RAG limitations

Enterprises can overcome RAG’s limitations to adapt AI at large-scale for their internal data while maintaining scalability, accuracy, and security by following the below technical aspects.


  • Distributed Retrieval Systems: Implement a distributed retrieval systems like Apache Solr or Elasticsearch to parallelize the search process across multiple servers. This can significantly improve search speed and handle large data volumes.
  • Data Sharding: Shard the data based on logical partitions (e.g., department, document type) to improve retrieval efficiency. This allows focusing searches on relevant data subsets.
  • Hierarchical Indexing: Hierarchical indexing structures that categorize data based on topics or entities are very useful to narrow down the search space and retrieve more focused information.
  • Vector Embeddings with Approximate Nearest Neighbors (ANN): Utilize vector embeddings for data representation and leverage ANN algorithms like FAISS or HNSW to find similar data points efficiently, even in massive datasets.


  • Semantic Search Techniques: Move beyond simple keyword matching and integrate semantic search techniques like sentence transformers or contextual embeddings (e.g., BERT) to understand the meaning of queries and data better. This leads to more relevant information retrieval.
  • Active Learning: Implement active learning techniques where the RAG system itself can query human experts for feedback on retrieved information. This feedback loop helps refine the retrieval process over time and improve accuracy.
  • Meta-data Integration: Enrich the data used by RAG with meta-data (e.g., document creation date, author) to enhance the context and relevance of retrieved information.


  • Data Access Control: Implement robust data access control mechanisms (e.g., role-based access control) to restrict access to specific data based on user permissions. This helps prevent unauthorized retrieval of sensitive information.
  • Query Validation and Sanitization: Validate and sanitize user queries before feeding them to the retrieval system. This helps prevent potential injection attacks or manipulation of the search process.
  • Homomorphic Encryption: Explore homomorphic encryption techniques that allow searching on encrypted data. This ensures data remains encrypted even during the retrieval process, enhancing security.

Tech stack

The specific tech stack usually depends on the enterprise’s existing infrastructure and the goals of AI. However, some potential technologies include:

  • Distributed Retrieval Systems: Apache Solr, Elasticsearch
  • Vector Embeddings: Sentence Transformers, Gensim
  • ANN Libraries: FAISS, HNSW
  • Active Learning Frameworks: scikit-learn (active_learning module)
  • Data Access Control Tools: Apache Ranger, Apache Knox
  • Homomorphic Encryption Libraries: HElib, SEAL

Additional Considerations

  • Monitoring and Logging: In the production environment continuously monitor the RAG performance to track the retrieved information and log user queries for analysis. This helps identify potential biases, security breaches, and areas for improvement.
  • Hybrid Approach: A hybrid approach where RAG is used for initial retrieval, and the human experts refining the information to provide additional context for the LLM is promising more effective results in the recent days.

Despite its limitations, RAG remains a valuable tool for enabling AI in enterprises. By acknowledging the limitations and implementing strategies to mitigate them, enterprises can harness the benefits of AI at large-scale for their business processes and services. Some key considerations are:

  • Focus on Specific Use Cases: Instead of trying to implement RAG for all tasks, focus on specific use cases where the retrieved information is crucial, and the data volume is manageable.
  • Data Cleaning and Indexing: Regularly clean and organize the data used by RAG. Implement effective indexing techniques to improve the efficiency and accuracy of the retrieval process.
  • Continuous Monitoring and Improvement: Continuously monitor the performance of RAG, identify and address biases in retrieved data, and update the knowledge base to ensure its relevance.
  • Layered Security Measures: Implement layered security measures to protect the retrieval system from potential vulnerabilities and ensure the integrity of the retrieved information.

By implementing these technical solutions and adopting a strategic approach, enterprises can leverage RAG’s capabilities to effectively work with large-scale internal data while maintaining scalability, accuracy, and security.


Here are few case-studies that demonstrate the above concepts in practice.

FinTech: Personalized Investment Recommendations with RAG

I collaborated with a team of engineers at a leading FinTech company to integrate RAG into their robo-advisor platform. The challenge was to personalize investment recommendations beyond basic asset allocation models. We implemented RAG to retrieve relevant financial news, analyst reports, and market data from the company’s knowledge base for each user query. The LLM, fueled by this retrieved information, could then generate personalized recommendations that considered current market trends, sector outlooks, and the user’s specific risk tolerance. This resulted in a significant increase in user engagement with the platform, higher conversion rates for investment recommendations, and improved customer retention.

  • Challenges: Integrating RAG with the existing platform and ensuring the security of the financial data used for retrieval were key hurdles.
  • Measurable Impact: Increased user engagement with the platform, higher conversion rates for investment recommendations, improved customer retention.

Healthcare: Clinical Decision Support using RAG for Improved Patient Outcomes

I was fortunate to be part of a project with a Healthcare solution provider focused on improving clinical decision support for doctors. We implemented RAG within their electronic health record (EHR) system. When a doctor entered a patient’s symptoms and medical history, RAG would retrieve relevant clinical research papers, treatment guidelines, and patient case studies from the hospital’s vast medical database. This real-time decision support empowered doctors with the latest medical knowledge, leading to faster and more accurate diagnoses, reduced medical errors, and improved patient outcomes. Measurable impacts included decreased time to diagnosis, improved accuracy of diagnoses, and a significant reduction in hospital readmission rates.

  • Challenges: Ensuring the accuracy and relevance of the retrieved medical data was paramount. We addressed this by implementing rigorous data cleaning and validation processes.
  • Measurable Impact: Reduced time to diagnosis, improved accuracy of diagnoses, decreased hospital readmission rates, improved patient satisfaction with care.

Retail: Dynamic Product Recommendations with RAG

I recently collaborated with engineers at a major e-commerce retailer to personalize product recommendations for their customers. The goal was to move beyond simple keyword matching and suggest products that truly catered to individual needs. We implemented RAG to retrieve customer reviews, product descriptions, and purchase history data. The LLM, armed with this contextual information, could recommend products that complemented the customer’s initial search and considered trending styles or seasonal preferences reflected in reviews. This data-driven approach led to a significant increase in click-through rates on product recommendations, improved conversion rates on product pages, and a higher average order value for the retailer.

  • Challenges: Developing a robust and scalable retrieval system to handle the massive amount of customer data was crucial. We addressed this by using a distributed search architecture with Apache Solr.
  • Measurable Impact: Increased click-through rates on product recommendations, improved conversion rates on product pages, higher average order value.


The true potential of RAG lies in its ability to be customized to various industry-specific needs and data sources. As RAG technology matures, we can expect to see more real-world implementations that unlock the power of LLMs for large enterprise AI across various sectors.

Get in touch to discuss how AI can be used in your business processes and services.

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