Generative AI has the potential to transform every industry. Human workers are already using large language models (LLMs) to explain, reason about…
Overview
This article outlines a structured approach to transitioning Retrieval-Augmented Generation (RAG) applications from pilot to production, emphasizing the role of NVIDIA AI in simplifying this process. It details four key steps and highlights the importance of collaboration among various stakeholders in the development and deployment phases.
What You'll Learn
1
How to evaluate LLMs using the NVIDIA API catalog
2
How to export a model as an NVIDIA NIM microservice
3
How to develop a sample RAG application using NVIDIA examples
4
How to deploy a RAG pipeline to production effectively
Prerequisites & Requirements
- Understanding of generative AI and RAG concepts
- Familiarity with NVIDIA AI tools and frameworks(optional)
Key Questions Answered
What are the four steps to move a RAG application from pilot to production?
The four steps include evaluating LLMs in the NVIDIA API catalog, exporting a model as a microservice, developing a sample RAG application, and deploying the RAG pipeline to production. Each step is designed to facilitate the transition from evaluation to a fully operational application.
How can enterprises simplify the development of RAG applications?
Enterprises can simplify RAG application development by utilizing NVIDIA's modular reference architecture, which integrates open-source software with NVIDIA acceleration. This architecture allows for selective integration of components, reducing complexity and avoiding vendor lock-in.
What role do NVIDIA tools play in RAG applications?
NVIDIA tools provide essential support for building and deploying RAG applications, including GPU-accelerated containers for performance, open-source integration for flexibility, and support for multimodal data processing to enhance application functionality.
Why do many RAG pilots fail to move into production?
It is estimated that 90% of RAG pilots do not progress beyond the evaluation phase due to challenges in transforming demos into production services that deliver real business value. These challenges include issues related to LLM security, usability, and data governance.
Key Statistics & Figures
Percentage of RAG pilots that fail to move into production
90%
This statistic highlights the significant challenges enterprises face in transitioning RAG applications from evaluation to production.
Technologies & Tools
Backend
Nvidia Nim
Used to export models as self-hosted microservices for generative AI.
Backend
Nvidia Nemo
Facilitates document embedding and retrieval functions in RAG pipelines.
Backend
Nvidia Rapids
Accelerates searching and indexing of databases that store vector representations of enterprise data.
Backend
Nvidia Riva
Provides GPU-accelerated text-to-speech and speech-to-text capabilities.
Backend
Nvidia Morpheus
Used for preprocessing large volumes of enterprise data in real time.
Backend
Nvidia Metropolis
Adds video and sensor processing capabilities to RAG pipelines.
Key Actionable Insights
1Leverage NVIDIA's API catalog to evaluate LLMs before deployment.This allows developers to interact with models and export API calls, ensuring that the chosen model meets performance and accuracy requirements.
2Utilize NVIDIA NIM to export models as microservices for easier deployment.This approach facilitates running models in various environments, including cloud and on-premises, enhancing flexibility and security.
3Incorporate NVIDIA Generative AI Examples to kickstart RAG application development.These examples provide a foundation for building applications that integrate seamlessly with NVIDIA's tools, streamlining the development process.
4Focus on collaboration among data scientists, developers, and engineers during the RAG application lifecycle.Effective communication and teamwork are crucial for addressing challenges and ensuring the successful deployment of RAG applications.
Common Pitfalls
1
Failing to adequately evaluate LLMs before deployment can lead to poor performance.
Without thorough evaluation, organizations risk selecting models that do not meet their specific needs, resulting in inefficiencies and potential failures in production.
2
Neglecting collaboration among stakeholders can hinder the development process.
When data scientists, developers, and engineers do not communicate effectively, it can lead to misaligned goals and increased challenges in deploying RAG applications.
Related Concepts
Generative AI
Retrieval-augmented Generation
Large Language Models
Cloud-native Architecture