9. Conclusion
In this notebook, we've demonstrated how to fine-tune OpenAI models for specific use-cases. We've also demonstrated how to use Qdrant and Few-Shot Learning to improve the performance of the model.

Aggregate Results
So far, we've looked at the results for each scenario separately, i.e. each scenario summed to 100. Let's look at the results as an aggregate to get a broader sense of how the model is performing:

Category	Base	Fine-Tuned	Fine-Tuned with Qdrant
Correct	44%	32%	44%
Skipped	0%	18%	5%
Wrong	9%	3%	4%
Hallucination	47%	7%	25%
I don't know	0%	40%	22%
Observations
Compared to base model
The few shot fine-tuned with Qdrant model is as good as the base model at answering questions where the answer is present in the context.
The few shot fine-tuned with Qdrant model is better at saying "I don't know" when the answer is not present in the context.
The few shot fine-tuned with Qdrant model is better at reducing hallucinations.
Compared to fine-tuned model
The few shot fine-tuned with Qdrant model gets more correct answers than the fine-tuned model: 83% of the questions are answered correctly vs 60% for the fine-tuned model
The few shot fine-tuned with Qdrant model is better at deciding when to say "I don't know" when the answer is not present in the context. 34% skip rate for the plain fine-tuning mode, vs 9% for the few shot fine-tuned with Qdrant model
Now, you should be able to:

Notice the trade-offs between the number of correct answers and hallucinations -- and how training dataset choice influences that!
Fine-tune OpenAI models for specific use-cases and use Qdrant to improve the performance of your RAG model
Get started on how to evaluate the performance of your RAG model