The presentation was supurlative. Google out did themselves by providing the links to the code repository where the code samples can be found.
The code in the jupyter notebook needs to be worked on a bit to arrive at the same results as in the presentation. This is an excellent learning exercise. I did the first half so that the code runs in google colaboratory and committed the code to git so that others can continue as well.
The following is a summary of the transcript provided by Gemini.
Presentation Summary: Grounding AI with Real-World Data
Introduction Speakers: Zind and Tristan, Google Cloud AI/ML experts. Topic: Grounding AI with real-world data, focusing on multimodal RAG and best practices.
Understanding LLMs LLMs (Large Language Models): Complex neural networks trained on massive amounts of data capable of generating text, translating languages, and answering questions. Limitations: LLMs are trained on publicly available data and may lack specific knowledge or real-time information.
Prompting: The art of crafting text input to guide LLM output. Techniques include zero-shot, one-shot, few-shot, and Chain of Thought prompting. Parameters: Temperature, top P, and top K influence LLM randomness and output.
Grounding Hallucinations: LLMs can generate incorrect or misleading information. Grounding: Aligning LLM output with real-world data to improve accuracy and reliability. Techniques: Fine-tuning, human-in-the-loop, and prompt engineering.
Retrieval Augmented Generation (RAG): Combining information retrieval with text generation to access external knowledge sources. RAG and Embeddings Problem: LLMs lack access to proprietary data, real-time information, and citation capabilities. Solution: RAG leverages embeddings to represent data as numerical vectors. Vector Search: Finding similar embeddings efficiently using approximate nearest neighbor (ANN) search.
Conclusion Grounding AI with real-world data is crucial for accurate and reliable LLM applications. RAG and embeddings are powerful tools for achieving this goal. By understanding these concepts, you can build more effective AI solutions.
Below are the links to the youtube video and the code repositories.