Caroline Boudier, senior product manager specializing in AI and machine learning (ML) at Dataiku, shared insights at Dataiku’s Product Days in October 2024 on retrieval-augmented generation (RAG). She highlighted why RAG is one of the most promising generative AI (GenAI) applications and how organizations can scale it beyond proof-of-concept (POC) to drive real business impact.
Many enterprises start their GenAI journey with RAG because it enhances large language model (LLM) accuracy, ensures traceability, and integrates private, up-to-date knowledge. While setting up a POC is straightforward, organizations are realizing that RAG alone is not enough. The next phase of enterprise AI requires RAG-powered agents that can retrieve, reason, and automate workflows across business functions.
Why RAG? Addressing Key LLM Challenges
LLMs are powerful but have key limitations:
- Lack of private company knowledge: LLMs rely on publicly available data and cannot access proprietary information.
- Hallucinations: AI-generated responses can sound credible but be entirely incorrect, posing risks for business use cases.
- Context window constraints: LLMs cannot process large internal documents like compliance policies or technical manuals.
RAG overcomes these challenges by retrieving relevant internal knowledge and augmenting LLM outputs with verifiable, company-specific information. This improves accuracy, reliability, and contextual relevance in AI-driven applications.
Real-World RAG Use Cases: Reducing Internal Bottlenecks
Organizations often implement RAG to eliminate knowledge bottlenecks that slow decision-making and increase inefficiencies. Caroline shared two real-world examples:
- LG Chem developed a RAG-powered web application to serve as the first level of support for employees searching for health and safety documentation. Automating responses to common inquiries reduced the workload on internal teams, allowing human experts to focus on complex cases.
- An international fashion company deployed a RAG-based chatbot that provided employees with instant access to corporate policies, procurement guidelines, and store operations manuals. Employees no longer had to manually search through documents; they received relevant responses instantly.
4 Common Traps When Scaling RAG
Trap #1: Underestimating Data Preparation
Many organizations assume that existing documents can be easily integrated into a RAG system. However, extracting structured and relevant information from messy, unstructured files — PDFs, slides, scanned reports — can be the most challenging step. Without proper data preparation, RAG models retrieve incomplete or irrelevant responses.
💡How Dataiku helps:
- Content extraction: OCR, text parsing, and a vision-LLM-assisted approach for handling tables and images. Supports a wide range of formats, including DOCX, PPTX, PDF, Markdown, HTML, and text.
- Advanced chunking: Fixed-size chunking, recursive chunking for hierarchical structures, and document-aware chunking that preserves sections in DOCX, PDFs, Markdown, and HTML.
- Tailored data preparation: 100+ text processing functions within Dataiku’s Prepare Recipe, a packaged agent for GraphRAG custom document preparation, and custom code options for semantic chunking and chunking-free embeddings (e.g., CoLPALI).
Trap #2: Relying Solely on Data Scientists
RAG requires collaboration between subject matter experts, software engineers, and end users. For example, compliance officers must validate AI-generated safety compliance responses. Without their input, AI models may misinterpret policies, leading to incorrect answers.
💡How Dataiku helps:
- No-code and low-code tools enable non-technical users to contribute to RAG projects.
- Dataiku Answers provides an intuitive chatbot framework for deploying RAG-powered applications without deep ML expertise.
- Agent Connect provides a unified interface to manage Dataiku Agents and Dataiku Answers, enabling organizations to deploy multi-agent AI systems that streamline workflows across different business functions.
- Flexible coding options allow advanced users to customize retrieval strategies while ensuring accessibility for all stakeholders.
Trap 3: Neglecting Evaluation and Monitoring
RAG-generated responses depend on retrieved documents, making evaluation and monitoring essential. Companies need to assess whether:
- The model retrieved relevant and high-quality information.
- Responses are accurate and based on the provided documents.
Without proper evaluation, RAG models may produce misleading outputs, reducing trust in AI-generated responses.
💡How Dataiku helps:
- LLM-specific evaluation metrics measure accuracy, hallucination risk, and retrieval quality.
- Real-time monitoring tools track model performance after deployment, allowing for continuous optimization.
Trap #4: Poorly Managing Risk and Usage Controls
As more employees interact with AI-driven applications, governance becomes a critical factor. Not all users should have access to all information, and companies need clear controls to ensure AI isn’t misused. For example, companies need to:
- Manage AI costs by setting usage quotas to prevent unnecessary computational expenses.
- Prevent security risks such as prompt injections, where users manipulate AI into revealing confidential information.
- Enforce access policies, ensuring employees can only retrieve authorized data while keeping sensitive information secure.
💡How Dataiku helps:
- Quota management limits AI usage per user or department to control costs and prevent overuse.
- RAG guardrails protect against hallucinations, unauthorized data access, and adversarial attacks with pre-configured safeguards.
With these latest features, enterprises can securely scale RAG while maintaining control, compliance, and cost efficiency
The Future of RAG in the Enterprise
Caroline’s session reinforced a key takeaway: RAG is a powerful technology, but scaling it requires more than a technical deployment. While setting up a POC is relatively simple, transforming it into an enterprise-grade solution demands strategic planning in data preparation, evaluation, governance, and collaboration.
RAG has transformed enterprise AI by enhancing LLM accuracy, but businesses are now evolving beyond standalone retrieval to AI systems that act, reason, and automate. The next frontier is multi-tasking AI agents that combine RAG with automation, decision-making, and advanced governance.
With Agent Connect, organizations can move beyond standalone RAG implementations and integrate them into multi-agent AI workflows, ensuring a seamless, scalable, and governed AI experience.
Scaling AI in the enterprise requires a strategic approach — organizations must integrate RAG-powered agents, ensure continuous monitoring, and establish robust governance to unlock AI’s full potential. This is where Dataiku — the Universal AI Platform — enables organizations to build, refine, and scale AI-driven agents that move beyond information retrieval to real enterprise impact.