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Voxxed Days Ioannina 2024 - Generative AI in the real world
Discover the limitations and best practices for implementing generative AI in real-world applications, including sanitizing input, fine-tuning models, and recognizing linguistic processing for accurate responses.
- Generative AI models have limitations, including token number limits and foreseeing 100% accurate answers.
- Models can hallucinate, providing false information, which can be detected by testing and evaluating the responses.
- To improve results, sanitize input, use prompt chaining, and fine-tune models.
- Consider architecture, tokenization, and coding strategy.
- Use APIs, exploring different models and frameworks, and consider caching and permission-based access.
- Ensure security, limiting exposure to potential risks and training models to provide accurate information.
- Consider case studies, testing models, and evaluating results, just like in the example from OpenAI’s book recommendation model.
- Prompts should be concise, structured, and follow a specific format.
- Use training data to optimize model performance.
- Recognition of linguistic processing and understanding is crucial for accurate responses.
- Detecting and correcting hallucinations is essential.
- Administrators should oversee model performance and user feedback.
- Neutrality and respect for language are important considerations when combining models.
- Access to and usage of vast amounts of data requires data centers, handling storage and scalability.
- Efficient dataframe manipulation is important.
- The power of generative AI lies in its ability to automatically complete a query with generated content.
- Natural Language Processing (NLP) and machine learning (ML) are related.
- Fine-tuning Models can improve their performance.
- Textual database extraction requires attention to type and content.
- Use User feedback loops.
- Making AI models understand the context of the query is significant.
- Existing problems should be adapted to a specific problem-solving strategy.
- be able to explain and recognize the importance of named data entities.