In my previous post, we talked about customizing general-purpose models like OpenAI's GPT-3.5 or GPT-4, transforming them into domain experts in areas such as finance or law. We established that fine-tuning is an effective strategy for achieving this.
However, there's another technique to enhance the performance of LLMs, enabling them to specialize in specific domains and tasks. In this post, we'll explore Retrieval-Augmented Generation (RAG) and its impact on large language models in depth.
Let’s explore further…
Retrieval-Augmented Generation (RAG) Overview
We’ve established in my prior post that LLMs possess a foundational knowledge base but can sometimes "hallucinate" facts while generating text. To mitigate this, it's crucial to supply the LLM with current and correct information. This can be achieved by searching for relevant information in a database and integrating this with the user's query, thus enhancing the generated text with accurate data.
Retrieval models excel at sifting through vast external knowledge bases to find pertinent information, while generative models are adept at using this information to craft new text. The RAG model's hybrid approach often yields more accurate and contextually relevant results compared to using retrieval and generative models separately.
How RAG Actually Works
If you consider the enormous amount of information within a given organization—structured databases, unstructured PDFs, blogs, news feeds, and the list goes on. RAG translates this vast array of dynamic data into a common format, stored in a knowledge library accessible to the generative AI system.
This data is processed into numerical representations using an embedded language model and stored in a vector database for quick retrieval, providing the correct contextual information for each query.
User Interaction: The user interacts with the system by submitting a query.
Data Aggregation: The system has access to 'Your data', which is collected from multiple sources:
Database: This represents structured data that resides in a relational database format.
Document: This refers to unstructured data, which could be text documents, PDFs, or other formats that do not have a rigid schema.
API: These are programmatic interfaces that allow the system to fetch data on-demand.
Indexing: Before the data can be used, it is indexed. Indexing is the process of organizing data in a way that makes it easily searchable. This index serves as an intermediary that can efficiently locate the relevant data across the various sources when a user submits a query.
Query Processing and Augmentation:
The user's query is received by the system.
The index is searched to find prompt-relevant data from the various data sources.
The system forms a composite input consisting of the user's original prompt, the query, and the relevant data retrieved from the index.
LLM Processing: The composite input is sent to the LLM.
The LLM processes the input to understand the context and the specifics of the query.
The LLM then generates a response based on the input received.
User Response: The response generated by the LLM is then sent back to the user.
RAG vs. Fine-Tuning
RAG and fine-tuning represent two distinct approaches to employing LLMs. RAG excels in dynamic environments by regularly incorporating the latest data from external knowledge bases, ensuring the generated information remains current. In contrast, fine-tuned LLMs can become outdated, as they represent static snapshots of their training data and may not retain acquired knowledge over time.
Fine-tuning is best used for addressing stable, long-term challenges, such as adapting the model to a specific domain. RAG shines in rapidly changing scenarios, offering up-to-date responses to new information.
Closing Remarks
The primary differences between RAG and fine-tuning lie in their complexity, architectural design, use cases, and customization. Currently, RAG technology is being utilized in chatbots, email, text messaging, and other conversational applications to provide timely, accurate, and contextually relevant responses.
RAG could significantly enhance generative AI's ability to take appropriate actions based on contextual information and user prompts. A RAG-augmented system might analyze real-time traffic and public transportation data to recommend the quickest commuting routes. Alternatively, it could support healthcare professionals by sifting through the latest research to suggest personalized treatment plans for patients, aligning with current medical guidelines. The potential for RAG to facilitate more sophisticated interactions and decisions is vast, marking an exciting direction for the future of generative AI.
With that being said, as more use cases continue to emerge, I look forward to documenting further developments in this space.
If you’re an investor or builder in the space and would like to connect, feel free to reach out to me at Ernest@Boldstart.vc or on twitter @ErnestAddison21