# The Fine Tuning Process
***
\
Fine-tuning is a process that enhances the performance of neural language models by precisely adjusting them for specific tasks.
Unlike prompt engineering, which directs a model without altering its internal structure, fine-tuning involves a deeper modification, adjusting the model's internal parameters, such as weights, to improve its task-specific performance.
This process not only sharpens the model's abilities, ensuring higher accuracy and reliability but also saves significant time and resources by leveraging pre-existing knowledge from pre-trained models.
Fine-tuning equips models with the flexibility to adapt to a wide range of tasks with minimal adjustments, making it an essential step in the model training pipeline.
After pretraining, fine-tuning refines the broad knowledge base of the model, aligning it with the particularities of a new task, which significantly optimises performance.
The process involves preparing diverse and relevant data, updating model weights through backpropagation, and carefully selecting hyperparameters to guide the model's learning.
Fine-tuning is not just an adjustment but an enhancement that ensures neural language models achieve task-specific precision and efficiency.
---
# 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://training.continuumlabs.ai/training/the-fine-tuning-process.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.