Next-Gen AI: The Power of RAG

RAG is an emerging AI technique designed to improve the output of large language models (LLMs) by accessing and incorporating information outside their training data sets before generating a response. RAG eliminates the need for both building a costly LLM from scratch and sending sensitive data to the cloud. This "data on demand" approach offers a secure and cost-effective alternative.

A typical AI request (called an inference) involves six basic steps:

Step 1 : Input data preparation - This could involve normalization, tokenization (for text), resizing images, or converting the data into a specific format.

Considerations for RAG-specific preparation:

• Prompt Engineering: Crafting clear and concise prompts that effectively guide the retrieval process is crucial. This might involve reformulating the user query or adding specific keywords to focus the search on relevant external knowledge.
• Data Type Compatibility: Ensure compatibility between the input data format (text, image, etc.) and the retrieval component's capabilities.

Step 2 : Model loading - This model has already been trained on a data set and has learned patterns that it can apply to new data.

This is where RAG shines! The prepared data is fed to the model, and additionally, RAG searches through authorized external sources like internal databases or documents.

Step 3 : Inference execution. The prepared input data is fed into the model.

Step 4 : Output generation. The nature of this output depends on the task.

In this step, RAG selects the most relevant retrieved documents and guides the LLM to generate a response tailored to the specific task.

Step 5 : Post-processing. The raw output from the model may undergo post-processing to convert it into a more interpretable or useful form.

Step 6 : Result interpretation and action. Finally, the post-processed output is interpreted within the context of the application, leading to an action or decision.

For example, in a medical diagnosis application, the output might be interpreted by a healthcare professional to inform a treatment plan.

In a RAG-augmented inference, RAG most affects steps 3 and 4. For example, in step 3, the application also searches whatever external data it’s been given access to (internal company databases, external documents, etc.) in addition to the training data the model was built on. Then, in step 4, RAG picks the top-matched documents from the retrieval step and uses the LLM to generate the response depending on the specific use case (i.e., question answering, summarization, etc.).

RAG : Pros & Cons

Pros:

• Reduced Development Time and Cost: Building your own LLM is a massive undertaking. RAG lets you leverage pre-existing LLMs and improve their outputs without the investment.
• Enhanced Privacy and Security: Sending data to the cloud can be a privacy concern. RAG injects relevant data directly into the model, keeping your sensitive information on your own systems.
• Improved LLM Performance: By providing contextually relevant data, RAG helps LLMs generate more personalized and accurate responses.
• Complements Prompt Engineering: Prompt engineering involves crafting effective prompts to guide the LLM. RAG works alongside this technique for even better results.

• Improved Accuracy: RAG helps LLMs avoid hallucinations by incorporating external knowledge during response generation, leading to more factually correct and reliable outputs.
• Enhanced Data Efficiency: RAG systems can perform well even with limited training data for the LLM, as they leverage the external knowledge base.
• Flexibility: RAG architectures can be adapted to various tasks like question answering, summarization, and more.
• Combat Bias: By using a diverse knowledge base, RAG can potentially mitigate biases present in the LLM's training data.

Cons:

• Complexity: RAG systems involve additional components like a retrieval module, making them more complex to set up and maintain compared to traditional LLMs.
• Knowledge Base Dependence: The quality and accuracy of retrieved information heavily depend on the comprehensiveness and correctness of the external knowledge base.
• Limited Control: The retrieved information can significantly influence the LLM's response, potentially reducing control over the final output compared to a standard LLM.
• Computational Cost: Retrieving information from external sources can add computational overhead compared to standard LLM inference.