Generative AI (genAI) has the potential to radically elevate customer experiences and streamline operations, delivering transformative impact across the enterprise, yet businesses encounter a significant challenge: the inherent limitations of foundational models. These models often struggle with delivering accurate and relevant outputs, primarily due to their constrained training datasets. Our latest Forrester report introduces retrieval-augmented generation (RAG) as a solution, integrating data indexing and knowledge retrieval with generative processes to overcome these challenges. This technology plays a crucial role in advancing genAI, supported by a growing ecosystem of software platforms.

The RAG Revolution: From Engine To Ecosystem

Leading technology vendors and forward-thinking enterprises are evolving their RAG engines — enhanced with essential core capabilities — into comprehensive, four-layer platforms designed to meet a broad range of real-world business needs. Infrastructure support streamlines integration with existing cloud and data infrastructure. Development enablement facilitates RAG-based application development, especially AI agents; platform operations provide manageability and observability for RAG adoption; and RAG governance offers guardrails for security, privacy, and regulatory compliance.

Navigating The Software Ecosystem

The ecosystem supporting RAG platforms is diverse, encompassing RAG platform builders, enablers, and service providers. Each plays a crucial role in the development and deployment of RAG technologies. From public cloud providers offering essential building blocks for RAG adoption to AI/ML platform vendors enriching RAG features, the landscape is rich and varied. Our report offers a comprehensive analysis of these players, providing businesses with the knowledge to choose the right partners for their RAG journey.

Practical Steps For Business Leaders

Adopting RAG isn’t just about leveraging new technology; it’s about transforming business operations to be more efficient, responsive, and intelligent. To this end, our report outlines four pragmatic steps for integrating RAG solutions:

  1. Data preparation. Ensuring that your data is AI-ready is foundational. Clean, structured, and ethically sourced data enhances RAG system performance.
  2. Optimization. Fine-tuning retrieval algorithms and prompt engineering can significantly improve the quality of generated outputs.
  3. Integration. Seamlessly integrating RAG systems with existing workflows and technologies is crucial for maximizing their utility.
  4. Human-centric design. Designing RAG systems with the end user in mind ensures that they meet real business needs and gain wider acceptance.

For business leaders, understanding and implementing RAG technologies is not just about staying ahead in the tech curve — it’s about redefining what’s possible with AI. RAG platforms offer the promise of intelligent automation, sophisticated data analysis, and enhanced customer interactions, among other benefits.

Embarking On Your RAG Journey

Our report, Forrester’s Guide To Retrieval-Augmented Generation, Part Two, serves as a roadmap for businesses looking to explore the vast potential of RAG. It provides not only an in-depth analysis of the current state of RAG technology but also practical advice for implementation and optimization.

Looking to further delve into how RAG can transform your business capabilities? Check out part one of this report series! Forrester clients can also schedule an inquiry with me for a tailored discussion on your RAG journey.