Prompting vs AI Model Fine-Tuning vs AI Embeddings

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Beyond simple prompt engineering, there are two design approaches to consider: building an embedding database of all proprietary content and dynamically searching for relevant information at runtime, or sending the content to the AI provider to fine-tune the model.

Feature AI Model Fine-tuning AI Embeddings
Purpose Improve the performance of a language model on a specific task Capture the meaning of text
Process Retrain the language model on a new dataset of data Calculate a numerical representation of the text
Applications Text generation, translation, question answering Search, classification, recommendation
Advantages Can improve the performance of a language model significantly Efficient and easy to use
Disadvantages Can be time-consuming and expensive May not be as accurate as fine-tuning



The best technique to use will depend on the specific task you want to perform and the resources you have available



  • Prompting: is the simplest technique. It involves providing the LLM with a text prompt that describes the task you want it to perform. The LLM then generates text that is consistent with the prompt. This is a very efficient technique, as it does not require any retraining of the LLM. However, it can be less accurate than fine-tuning or embeddings, as the LLM may not be able to understand the prompt perfectly.
  • Fine-tuning: is a more powerful technique than prompting. It involves retraining the LLM on a dataset of examples for the specific task you want it to perform. This can improve the accuracy of the LLM, but it also requires more training data and compute resources.
  • Embeddings: are a middle ground between prompting and fine-tuning. They involve using a small model to learn a representation of the input text. This representation is then used to initialize the LLM, which can then be fine-tuned on a dataset of examples for the specific task you want it to perform. This can improve the accuracy of the LLM over prompting, without requiring as much training data or compute resources as fine-tuning.


Technique Performance Efficiency Flexibility
Prompting Low High High
Fine-tuning High Low Low
Embeddings Medium Medium Medium