Enterprise adoption of GenAI is surging—but most projects stall when it’s time to securely connect to real data. 

RAG: Solving the Enterprise LLM Challenge

The rise of large language models (LLMs) has sparked a transformative shift in how enterprises harness AI to drive efficiency, innovation, and automation. These models offer unprecedented capabilities in natural language understanding and generation, and have applications across every line of business—sales, support, engineering, etc.

Yet, despite their power, deploying LLMs in an enterprise setting presents significant challenges—among them, the ability to provide accurate, real-time, and contextually relevant responses using an organization's proprietary data.

This is where RAG has emerged as the go-to architecture for integrating enterprise data with LLM applications. Unlike fine-tuning, which is resource-intensive and often static, RAG dynamically retrieves relevant information from external knowledge sources at query time, ensuring that responses are both current and grounded in enterprise-specific data. 

This architecture not only enhances accuracy but also improves compliance, security, and scalability—key concerns for enterprises looking to operationalize AI responsibly.

Getting Started with RAG

from langchain import …
…
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")
llm = ChatOpenAI(model_name="gpt-4o", temperature=0)
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
)
rag_chain.invoke("How easy can I build a RAG pipeline?")

Adapted from langchain-ai rag-from-scratch

With these frameworks, developers can get a working prototype up and running in hours rather than weeks, making it easy to showcase the power of LLMs when combined with enterprise data. This rapid development cycle allows organizations to quickly validate use cases such as semantic search over internal documentation, AI-powered customer support, or automated report generation—demonstrating immediate value with minimal upfront investment.

Challenges of Moving from Prototype to Production

While building a RAG prototype is relatively easy, scaling it into a production-ready enterprise system introduces a range of technical and operational challenges. Beyond just retrieving and generating text, a robust RAG system must integrate with existing enterprise infrastructure, adhere to security and compliance policies, and optimize for performance and cost. Let’s explore some of the key challenges organizations face when transitioning RAG from a prototype to a production deployment.

Connecting to the Data Sources Where Enterprise Knowledge Lives: A RAG system is only as good as the data it can access, but enterprise knowledge is often scattered across multiple systems—document repositories, databases, CRMs, wikis, and issue tracking systems. Unlike a prototype that might work with static files or a single source, a production deployment must seamlessly connect to various data sources, each with different formats, access protocols, and query mechanisms. This requires building and maintaining connectors that can efficiently retrieve and update data without disrupting existing workflows.

Periodic Refresh to Keep the Content Up to Date, Including Knowledge Deletion: Enterprise knowledge is constantly evolving—new policies, updated reports, and fresh customer interactions continuously change the knowledge landscape. A prototype often relies on a static dataset, but a production system must refresh content to ensure responses reflect the latest information. Beyond adding new data, enterprises must also handle knowledge deletion, ensuring that outdated or sensitive information does not persist in the retrieval system. Managing these updates at scale, without excessive downtime or performance degradation, is a significant operational challenge.

Expensive Data Copies: Many RAG implementations rely on creating copies of enterprise data—increasing storage costs. In addition to the storage overhead, copying large volumes of data introduces compliance and security risks. Furthermore, copying the data into a separate data lake loses the source access control mechanism.

Maintaining Document Permissions: A major hurdle in production RAG deployments is enforcing document-level access control. Enterprise data is often protected by fine-grained permissions that determine which users can view specific documents. A naive RAG implementation risks exposing restricted information if it retrieves data without honoring access policies. A production system must integrate with enterprise authentication and  authorization (e.g., SSO, role-based access control) and ensure that retrieval results only include documents the user is authorized to see—a non-trivial engineering challenge.

Exposing Sensitive Data: Even if access controls are in place, it is often desirable to suppress sensitive information in both prompts to the LLM and completions generated for the end user. This could include personally identifiable information (PII) or confidential financial account numbers. Since RAG enhances LLM outputs with retrieved enterprise knowledge, the risk of sensitive data leakage increases. Mitigating this requires robust content filtering and redaction mechanisms. A production-ready system must include automated safeguards that detect and block unintended disclosures before they reach end users.  

Organizational Sign-off: As a developer of a RAG-based system, you’d likely have to work with IT to set up data access and pipelines, Finance and Procurement to manage infrastructure and tooling costs, and Information Security and Legal for privacy and security concerns.

In addition to the technical challenges associated with productionizing RAG, there is a significant organizational hurdle: Gartner predicts that by the end of 2025 “[a]t least 30% of generative AI (GenAI) projects will be abandoned after proof of concept.” Gartner cites data quality, risk, cost, and vague strategy as significant blocks to substantive AI adoption. 

Rubrik Annapurna: AI Built on a Secure Data Foundation

# Import Rubrik Annapurna retrievers from rubrikinc import annapurna...
# Create and install Annapurna in RAG chain.
os.environ['ANNAPURNA_ENDPOINT'] = 'https://.my.rubrik.com' 
os.environ['ANNAPURNA_API_KEY'] = <your-api-key>annapurna_retriever= annapurna.new_retriever(retriever_id)rag_chain = (
    {"context": annapurna_retriever, "question": RunnablePassthrough()
    | prompt
    | llm
)
rag_chain.invoke("How easy can I build a RAG pipeline?")

In subsequent posts, we’ll dive into how Rubrik Annapurna delivers each of these capabilities in more detail. Annapurna integrates seamlessly with Amazon Bedrock, Azure OpenAI, and Google Agentspace.

If you’re a developer with a killer use case for RAG, check with your IT department to see if you’re already a Rubrik customer—and quickly move your app from prototype to production!