RAGs to Riches: How Retrieval-Augmented Generation enables better, faster, and cheaper AI solutions

Introduction

AI Models like ChatGPT provide API's to enable custom AI solutions. But standalone use of those API's often has limitations. Retrieval-Augmented Generation (RAG) is an AI architecture which helps overcome many of those limitations by enabling custom AI solutions that are better, faster, and cheaper.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture that combines the capabilities of AI Models with external knowledge sources to produce more accurate and contextually relevant outputs. Here’s how it works:

1. Retriever: A service which queries a Knowledge Base for additional supporting data
2. Knowledge Base: A datastore that provides access to external data sources such as PDFs, databases, structured datasets, or unstructured repositories
3. Generator: The AI Model which processes the user query with the retrieved context to produce a response

This architecture bridges the gap between static knowledge locked within AI Models and the dynamic, up-to-date information needed for practical applications.

Why use RAG?

1. Provides domain-specific knowledge

Problem: General-purpose AI Models like ChatGPT may lack domain-specific knowledge.

Solution: A RAG architecture augments a pre-built model like ChatGPT with relevant, domain-specific data, enabling the system to provide more accurate and tailored responses.

2. Ensures the most current and relevant data

Problem: The training data for an AI Model reflects a snapshot in time, and may not include more recent updates or changes.

Solution: A RAG architecture can help bridge this gap by augmenting the AI Model with the most recent data available.

3. Handles large contexts more effectively

Problem: AI Model APIs have constraints on token length, which can make it difficult to handle large datasets, complex queries, or extensive context within a single request.

Solution: A RAG architecture can be used to pre-process the intial request to reduce the size of the input data.

4. Maintains context across interactions

Problem: Standalone AI models often lack the ability to natively manage and reuse context across interactions, which can lead to fragmented or repetitive responses.

Solution: A RAG architecture can be used to retrieve and maintain relevant context for more coherent and connected interactions.

5. Reduces Query Costs

Problem: Querying large AI models repeatedly for complex or large-scale tasks incurs high computational costs, especially as the scale of usage increases.

Solution: RAG minimizes query costs by retrieving targeted information from a Knowledge Base in real time, reducing the frequency and load of expensive model queries.

6. Lowers Fine-Tuning Expenses

Problem: Adapting AI models to specific use cases or new data typically requires fine-tuning, which is resource-intensive and expensive.

Solution: RAG reduces the need for fine-tuning through independent updates to Knowledge Bases.

7. Scales Easily with Minimal Effort

Problem: Scaling traditional AI Models can be challenging and resource-intensive, often requiring costly retraining.

Solution: RAG simplifies this process by allowing new data sources to be added to the Knowledge Base without retraining the model.

8. Improves Accuracy

Problem: Standalone AI models can hallucinate or produce incorrect information due to their fixed training data.

Solution: By leveraging real-time data retrieval, RAG ensures responses are grounded in factual, relevant information.

Conclusion

Retrieval-Augmented Generation (RAG) is an AI architecture that has become widely adopted for implementing custom AI solutions. By enabling better, faster, and cheaper AI solutions, RAG unlocks opportunities to deliver high-value, high-impact AI innovations.

Next Steps

Are you interested in understanding how a RAG AI architecture can help your business deliver state-of-the-art AI solutions? Then feel free to reach out to [email protected] for a free consultation!