> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # What is Fine Tuning > This article explains fine-tuning of large language models and its advantages over dynamic context injection for incorporating up-to-date, domain-specific data. In this lesson, we’ll define **fine-tuning** and compare it with **dynamic context injection**. Pre-trained large language models (LLMs) are trained on massive, but sometimes outdated, datasets—GPT-3.5, for example, has a knowledge cutoff of September 2021. You can append fresh data to a prompt, but you’ll quickly hit the context window limit. GPT-3.5 Turbo only supports about 4 k tokens, and even 16 k-token windows can force you to chunk inputs, manage state, and suffer extra latency. ## Why Fine-Tuning? Rather than repeatedly attaching external context to every API call, fine-tuning lets you **retrain** an existing model on your own up-to-date, domain-specific data—PDFs, web pages, CSVs, or any other format. The model’s parameters internalize your private information, eliminating token-window headaches and simplifying your application logic. ![The image explains fine-tuning, showing a person with icons for LLMs and datasets, and describes it as an alternative to using dynamic context with prompts. It suggests considering fine-tuning when frequently adding external data to prompts.](https://kodekloud.com/kk-media/image/upload/v1752881506/notes-assets/images/Mastering-Generative-AI-with-OpenAI-What-is-Fine-Tuning/fine-tuning-llms-datasets-explained.jpg) If you’re attaching external data to nearly every prompt, fine-tuning is probably the better path. ![The image lists key reasons to fine-tune models, including retraining with refreshed data, overcoming context length limitations, avoiding dynamic context overhead, increasing response speed, and achieving higher quality responses.](https://kodekloud.com/kk-media/image/upload/v1752881507/notes-assets/images/Mastering-Generative-AI-with-OpenAI-What-is-Fine-Tuning/fine-tuning-models-reasons-list.jpg) Fine-tuning can dramatically cut latency and reduce prompt-management complexity when your application relies on frequent data updates. ## Key Advantages of Fine-Tuning | Advantage | Description | | -------------------------------- | ---------------------------------------------------------------------------------------------------- | | Retraining with Refreshed Data | Updates the model’s knowledge base using your custom dataset—no need for full-from-scratch training. | | Overcoming Context-Length Limits | Embeds data directly into model parameters, bypassing token-window constraints. | | Reduced Prompt Overhead | Eliminates bulky prompt payloads, cutting latency and simplifying your code. | | Faster, More Responsive | Delivers answers quickly because the model “knows” your domain out of the box. | | Higher Quality and Accuracy | Produces precise, use-case–aligned responses with your data baked in. | This walkthrough will cover the end-to-end process of fine-tuning an OpenAI model, from data preparation to deployment. *** ## Links and References * [OpenAI API Documentation](https://platform.openai.com/docs) * [Large Language Models (LLMs)](https://en.wikipedia.org/wiki/Large_language_model) * [OpenAI Fine-Tuning Guide](https://platform.openai.com/docs/guides/fine-tuning)