Enhancing AI Comprehension: A Technical Guide

The Problem

AI models, like those from OpenAI, are proficient at understanding and retrieving information from the documents they’ve been trained on. However, they encounter difficulties when dealing with concepts, contexts, and inferred meanings not explicitly present in their training data. The challenge is to expand their understanding and response capabilities without resorting to excessive fine-tuning, temperature adjustments, or exposing them to the whole internet.

The Solution: Deepening Comprehension through Complementary Content

The proposed method to address this issue is to deepen the AI’s comprehension by providing complementary content that augments its understanding of the core subject matter. This process involves three key steps:

Step 1: Establish a Solid Foundation with Core Documents

The first step is to train the AI model on the core documents related to the subject matter. This establishes a solid foundational understanding. For instance, in a real estate law application, the core documents would include statutes, regulations, and case law.

Step 2: Augment Understanding with Complementary Content

Next, identify additional materials that complement the core documents. These could include definition dictionaries, blog posts, case summaries, FAQs, etc. These resources provide context and connections that are not explicitly outlined in the core documents. When added to the AI’s training data or embeddings database, these materials fill the gaps in the AI’s understanding.

Step 3: Develop a Fact Sheet for Quick Reference

Finally, create a fact sheet for the AI to reference. This sheet should contain key facts or “answers”, each accompanied by multiple related questions. When this fact sheet is added to the embeddings database, it expands the range of questions the AI can answer accurately.

The Role of RAG (Retrieval-Augmentation Generation)

The Retrieval-Augmentation Generation (RAG) system plays a crucial role in this process. It uses the embeddings database to retrieve relevant information and generate responses. The addition of complementary content and fact sheets to the embeddings database enhances the RAG’s ability to provide more accurate and contextually relevant responses.

The Outcome

By incorporating these complementary materials, the AI gains a deeper understanding of the core concepts and their interrelations. This allows it to better handle questions and scenarios that go beyond the explicit facts found within the base training data. This method proves more effective than just fine-tuning, temperature tweaking, or exposing the model to the internet.

The Takeaway

AI models are only as smart as the data we feed them. While they can comprehend vast amounts of text, they struggle with making inferences from sparse or strictly factual data. By supplementing core documents with explanatory materials, we can expand the AI’s understanding and enable it to handle a broader range of contextual questions. This method of curating complementary content to deepen comprehension has proven effective for enhancing AI applications, making them more intuitive, helpful, and human-like.