Hi @finzer
While training a model with a new dataset can be effective, it often requires a large and well-designed dataset for optimal results. If these conditions aren’t met, the output might not be as satisfactory as expected.
Alternatively, we’d like to suggest the use of ‘Embeddings‘ instead of fine-tuning your AI. Embeddings convert your data into numerical vectors that can be understood by the AI model. It’s a more simplified, cost-effective, and speedy method to feed your content to the bot.
Here are the key differences between fine-tuning and embeddings:
- Fine-tuning: It involves training the entire model on your data. This requires a large and diverse dataset and considerable computational resources, making it a slow and expensive process.
- Embeddings: It involves creating numerical representations (vectors) of your data that can be easily understood and processed by the AI. This doesn’t require retraining the model, making it faster, cheaper, and easier.
With just a few clicks, you can use a vector database, like Pinecone, to input all your content to your bot. The chatbot then uses these embeddings to generate responses, allowing it to understand and answer a broader range of queries based on your data.
OpenAI itself recommends using embeddings for most applications, for the same reasons outlined above. The trade-off between the time, cost, and complexity of fine-tuning often doesn’t yield substantially better results compared to the use of embeddings.
I would strongly suggest giving embeddings a try for your chatbot. If you need assistance with this process, please don’t hesitate to ask. We’re here to help you make the most out of our AI tools.
Here are some documentation:
https://docs.aipower.org/docs/embeddings
Best regards,
