GPTs == APPs // LLM + RAG(DATA) + FUNC(API/TOOL) + CODE(GEN/RUN) + DB
Data is king — always has been. Anyone can now make it accessible in a LLM chat.
Settings in the Configure tab:
Adding an image: You can ask the GPT Builder to create an image for your GPT or you can upload your own under the Configure tab.
Additional Instructions: Here you can provide detailed instructions or guidelines on how the GPT should behave, its functionalities, and any particular behaviors to avoid.
Prompt Starters: These are examples of prompts for the user to start the conversation.
Knowledge: This allows you to provide additional context for your GPT to reference. Please note that content from the files that are uploaded could be included in the output.
New Capabilities: Enabling Web Browsing, DALL·E Image Generation, and Advanced Data Analysis, will allow the GPT to perform additional functionality.
Custom Actions: You can make third-party APIs available to your GPT by providing details about the endpoints, parameters, and a description about how the model should use it. Actions for GPTs can also be imported from an OpenAPI schema. So if you’ve already built a plugin, you will be able to use your existing plugin manifests to define actions for your GPT.
GPTs == Apps
GPTs
GPTs are custom versions of ChatGPT that users can tailor for specific tasks or topics by combining instructions, knowledge, and capabilities. They can be as simple or as complex as needed, addressing anything from language learning to technical support. Plus and Enterprise users can start creating GPTs at chat.openai.com/create. GPT’s live inside of ChatGPT and are intended to be created by anyone using a simple UI.
Assistants
The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. Assistants are designed to be created by developers using the OpenAI API.
Open alternatives // DIY
We’re going to test two popular 7B parameter models: Falcon 7B and Mistral 7B. You can find many more open-source LLMs to play around with on the Open LLM leaderboard.
Large language models (LLMs) demonstrate powerful capabilities, but they still face challenges in practical applications, such as hallucinations, slow knowledge updates, and lack of transparency in answers. Retrieval-Augmented Generation (RAG) refers to the retrieval of relevant information from external knowledge bases before answering questions with LLMs. RAG has been demonstrated to significantly enhance answer accuracy, reduce model hallucination, particularly for knowledgeintensive tasks. By citing sources, users can verify the accuracy of answers and increase trust in model outputs. It also facilitates knowledge updates and the introduction of domain-specific knowledge. RAG effectively combines the parameterized knowledge of LLMs with non-parameterized external knowledge bases, making it one of the most important methods for implementing large language models. This paper outlines the development paradigms of RAG in the era of LLMs, summarizing three paradigms: Naive RAG, Advanced RAG, and Modular RAG. It then provides a summary and organization of the three main components of RAG: retriever, generator, and augmentation methods, along with key technologies in each component. Furthermore, it discusses how to evaluate the effectiveness of RAG models, introducing two evaluation methods for RAG, emphasizing key metrics and abilities for evaluation, and presenting the latest automatic evaluation framework. Finally, potential future research directions are introduced from three aspects: vertical optimization, horizontal scalability, and the technical stack and ecosystem of RAG.1
