# Overview The success of Generative AI hinges on methods such as fine-tuning and in-context learning. Ensuring its adaptability to specific domains allows a broader range of individuals and businesses to benefit without deep technical expertise. ## **What's Texti.ai about?** This simple mindmap illustrates how a response (or completion) to the prompt "*Tell me about the drug Aspirin?*" can be altered when the foundational AI model is customized using In-Context Learning or Fine-tuning.

It's worth noting that **In-Context Learning** and **Fine-tuning** often necessitate substantial coding, data preparation, and domain-specific knowledge in data science. However, given the current scarcity of AI talent, there's an emerging need for tools that empower individuals to harness the capabilities of AI models without the necessity of coding.\ \ [Texti.ai](https://www.texti.ai/), recognizing this challenge, offers businesses and individuals the ability to delve into both fine-tuning and in-context learning—two key pillars of AI customization—without writing a single line of code. Simply upload your dataset, define the desired task, and Texti.ai handles the intricate processes behind the scenes. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.texti.ai/overview.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.