• Novaneuron
  • Posts
  • Fine-tuning OpenAI GPT 3.5 - Fine-tuning vs Embeddings

Fine-tuning OpenAI GPT 3.5 - Fine-tuning vs Embeddings

OpenAI has just dropped a game-changing update set to revolutionize how we harness the potential of AI.

OpenAI has just dropped a game-changing update set to revolutionize how we harness the potential of AI.

Imagine being able to mold the AI model to fit your specific needs, and in doing so, achieve exceptional performance on targeted tasks.

OpenAI now allows fine-tuning to GPT-3.5 Turbo

OpenAI writes

This update gives developers the ability to customize models that perform better for their use cases and run these custom models at scale. Early tests have shown a fine-tuned version of GPT-3.5 Turbo can match, or even outperform, base GPT-4-level capabilities on certain narrow tasks.

What does it mean for AI builders?

In simplest terms, this gives an additional option other than vector embeddings to train and use GPT on private data for various business use cases.

In our previous post, we discussed the technology stack of training an LLM on private data. Well, now, with GPT 3.5, you have one additional way to do it.

Here are a few ways fine-tuning can help applications

Enhanced Precision and Consistency: Fine-tuning empowers businesses to refine the model's response accuracy, ensuring it adheres to instructions like language preferences and concise output generation.

Uniform Output Structure: Fine-tuning strengthens response formatting for specific applications like code completion or API call creation. This assures developers of reliably converting user prompts into well-structured JSON snippets.

Tailored Brand Expression: Fine-tuning refines the model's output quality, enhancing its alignment with the brand's unique tone. This is especially valuable for businesses seeking a consistent brand voice.

How do the methods compare?

Beyond straightforward prompt formulation, there are two distinctive strategies to contemplate:

  1. The creation of an embedding database for proprietary content, with dynamic real-time search capabilities

  2. Entrusting OpenAI to fine-tune the model with your content.

Approach 1: Building an Embedding Database and Dynamic Search


  • Data Control: Full control over sensitive proprietary data.

  • Flexibility: Easy updates without retraining.

  • Cost-Effective: Lower cost compared to fine-tuning.

  • Speed: Quick implementation and turnaround.


  • Response Cohesion: Responses may lack coherence.

  • Complexity: Searching mechanism complexity.

The embedding approach will be better suited for cases where there are very large documents to be added as context. But if the data is too complex, it might lead to hallucinations.

Also, the embedding path works best if the data is fairly structured. For completely unstructured data, there is a higher possibility of errors and lack of cohesion.

Approach 2: Fine-Tuning with OpenAI


  • Integration: Cohesive, contextually relevant responses.

  • Quality: Improved response accuracy and quality.

  • Simplicity: Minimal development effort.


  • Data Privacy: Potential data exposure.

  • Cost/Time: Higher cost and time investment.

  • Updates: Requires iterative updates.

Works best for smaller documents, for considerations of cost and effectiveness. The fine-tuning path can handle more complex and unstructured data too.

Developing story… Stay tuned for more updates on this!