As you're here, it's quite likely that you're already well-informed about the wonders of Generative AI possibly through tools like ChatGPT, DALL-E or Azure OpenAI. If you've been surprised by the capabilities of this technology and wondered,"Hey, this tech is awesome, but how do I boost the response quality?"—you're in the right place.. Understanding how they can help us to unlock the full potential of our AI-powered applications.
I do recommend fostering a culture of prompt engineering and encouraging teams to share best practices and learnings in crafting effective prompts. This collaborative approach can significantly enhance effectiveness, boost productivity and lead to smarter decision-making. It's an investment that pays off in more ways than one!
Fine-tuning involves furnishing the model with a dataset containing labeled examples pertinent to the specific task at hand. My learning is that successful fine-tuning lies in the additional data and training it entails. This includes providing feedback or rating the AI's responses and steering the system with meticulously designed questions using prompt engineering techniques.
• Relying solely on prompt engineering can be challenging when striving for high accuracy and quality, as LLMs may still generate irrelevant or erroneous outputs. Fine-tuning becomes invaluable in enhancing the accuracy and quality of the model's outputs. It also mitigates the risk of generating irrelevant or contradictory responses which translates into customer satisfaction.