RAG vs Fine-Tuning vs Prompt Engineering Optimizing AI Models
AI Summary
Summary of the Video
Topic: Improving Large Language Model Responses
- Introduction to Current Equivalent of Vanity Searching
- Traditional self-searching on Google is now mirrored in querying chatbots.
- Responses from large language models (LLMs) vary significantly based on training data and knowledge cutoff.
- Improving Model Responses
- Methods to Enhance Responses:
- Retrieval Augmented Generation (RAG):
- Perform searches for recent or supplemental data to improve answers.
- Follows a process: retrieval of data, augmentation of the original query with retrieved information, and generation of a response.
- Uses vector embeddings to find documents semantically similar to the query.
- Pros: Access to up-to-date, domain-specific information.
- Cons: Increased latency and computational costs due to additional steps (retrieval and processing).
- Fine Tuning:
- Customize an existing model with additional specialized training data.
- Adjust internal parameters to develop expertise on focused topics.
- Pros: Faster inference times and deeper domain expertise.
- Cons: Requires extensive training data, high computational cost, and may lose general capabilities (catastrophic forgetting).
- Prompt Engineering:
- Direct the model’s focus through refined prompts, activating learned patterns without additional training.
- Examples must clarify expectations to improve outcomes.
- Pros: Immediate results; no backend infrastructure changes needed.
- Cons: Limited to existing knowledge and potential for trial and error in refining prompts.
- Combination of Approaches:
- Effective systems may integrate RAG, fine-tuning, and prompt engineering to optimize performance in areas such as legal AI, balancing flexibility, knowledge extension, and expertise.
- Conclusion:
- Advancements in LLMs demonstrate a shift from basic searching to complex interactions with AI, necessitating strategies to enhance their capabilities effectively.