Choosing between Retrieval-Augmented Generation (RAG) and fine-tuning depends on the specific requirements, resources, and goals of your application. Here are scenarios when each approach is more sensible.

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Retrieval-Augmented Generation (RAG) and fine-tuning are two approaches used in the development and enhancement of language models. Each has its own strengths, applications, and considerations. Here’s a detailed comparison. Let’s have a look. …

In the context of Retrieval-Augmented Generation (RAG) in AI, chunking refers to the process of dividing a large body of text or a dataset into smaller, manageable segments or “chunks” before feeding them into the system. This is particularly important in scenarios where a model needs to retrieve relevant information from a large corpus to generate accurate and contextually appropriate responses.
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RAG stands for Retrieval-Augmented Generation. RAG is a technique used in natural language processing (NLP) that aims to improve the quality of generated text by incorporating external information which the system retrieves from a large corpus aka documents or databases. Let’s take a closer look at how RAG works.
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In this article we are going to learn more about inpainting, more specifically about adding an object to the generated image. But let’s start with the first question that comes is “what is inpainting with DALL-E”?

Inpainting with DALL-E refers to the process of modifying an image by replacing or editing specific parts of the generated image using the capabilities of the DALL-E model. In this article we are going to add an object to the generated image.
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In this article we are going to learn more about inpainting, so the first question that comes is “what is inpainting with DALL-E”?

Inpainting with DALL-E refers to the process of modifying an image by replacing or editing specific parts of the generated image using the capabilities of the DALL-E model. In this article we are going to remove an object from the image. …

OpenAI’s GPT-4o, the latest addition to its line of advanced AI models, stands out with its enhanced capabilities across text, voice, and vision. Designed to deliver faster and more cost-effective solutions, GPT-4o is rapidly becoming a preferred choice for various AI applications. In this article we will explore GPT-4o, its main capabilities and limitations. And if you would like to read more about GPT-4o vs GPT Turbo, you’re invited to read the article in the link.
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OpenAI’s continuous evolution in AI technology has brought about significant improvements and variations in its models, specifically between GPT-4o and GPT-4 Turbo. Here’s a detailed comparison to help understand the key differences between these two advanced models.
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This was my “remodeling my living room” project 🙂 So, I took a picture of our small living room and asked DALL-E to change the accenting wall behind sofa to yellow. What I expected DALL-E, a model that generates images, is to edit the existing photo I uploaded. But is that even possible?
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Recently, I had to inspect quite large MySQL database. I wanted quick analysis of the database. First thought was what tool to use for such task? And there are many..

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