Large language models (LLMs) can do more than just answer simple questions. Companies do not have to rely on public offerings like ChatGPT. They can use streamlined LLMs that run in their own data centers. With Retrieval Augmented Generation (RAG), companies can include internal documents in their queries without exposing them externally.
Learn how LLMs work, how they can be expanded with RAG, and how agent systems function. The program of the online conference organized by iX and dpunkt.verlag offers lectures on the following topics:
- Introduction to Large Language Models
- Document preprocessing and embeddings
- Efficient retrieval in RAG systems
- Prompting and system prompts for AI engineers
- Evaluating RAG systems
- Introduction to agent systems
The program is rounded off with the development of a sample application. This includes data extraction, choosing the embedding model, the method for storing vectors, and implementation.
Tickets for the theme day are available at an early bird price of 249 euros until February 19. On March 19, there is also a full-day online workshop titled “Fine-tuning and Using Your Own Language Models,” which costs 549 euros.
Those who want to stay informed about the progress of Minds Mastering Machines and the associated theme days can sign up for the newsletter. The special day also has its own presence on LinkedIn.
Large language models (LLMs) represent a significant advancement in artificial intelligence, offering capabilities that extend beyond simple question answering. These models have revolutionized the way businesses interact with data, providing tools that can process and understand human language at an unprecedented scale. While public offerings like ChatGPT are widely known, companies have the option to deploy more streamlined LLMs within their own infrastructure. This approach ensures data privacy and allows for more tailored applications.
One of the key technologies enhancing the utility of LLMs is Retrieval Augmented Generation (RAG). RAG enables the integration of internal documents into AI queries, allowing businesses to leverage their proprietary information without compromising confidentiality. This method enhances the relevance and accuracy of responses generated by LLMs, making them more useful for enterprise applications.
Understanding the inner workings of LLMs and the potential of RAG involves exploring several technical aspects. These include document preprocessing, which prepares data for analysis by converting it into a format that LLMs can understand. Embeddings are another crucial component, as they transform words into numerical representations that capture semantic meaning, facilitating more nuanced language processing.
Efficient retrieval in RAG systems is essential for ensuring that the most relevant information is accessed quickly, enhancing the performance of LLMs in real-time applications. Prompting and system prompts are techniques used by AI engineers to guide the behavior of LLMs, ensuring they respond appropriately to various queries.
Evaluating RAG systems involves assessing their effectiveness in retrieving and incorporating relevant information, which is critical for optimizing their performance. Agent systems, which are another focus area, involve autonomous entities that can perform tasks on behalf of users, further expanding the capabilities of LLMs.
The practical application of these technologies can be seen in the development of sample applications. These projects typically involve data extraction, selecting suitable embedding models, determining methods for storing vectors, and implementing the final solution. Such applications demonstrate the real-world potential of LLMs and RAG systems in various industries.
For businesses and professionals interested in leveraging these technologies, participating in workshops and conferences can provide valuable insights. These events offer opportunities to learn from experts, explore new developments, and gain hands-on experience in deploying LLMs and RAG systems effectively.
In conclusion, large language models and Retrieval Augmented Generation represent transformative technologies in the field of artificial intelligence. By understanding and utilizing these tools, companies can enhance their data processing capabilities, improve decision-making, and maintain a competitive edge in an increasingly data-driven world.