I’ve been studying for the new Oracle Cloud Infrastructure (OCI) Generative AI Professional Certification, and I’ve found it to be one of the most interesting certifications I’ve done for a while. The exam covers some of the core fundamentals of Large Language Models and RAG Retrieval Augmented Generation, and within these, there are many new concepts to grasp and understand. https://blogs.oracle.com/ai-and-datascience/post/ga-oci-generative-ai
For those familiar with Cloud, then your existing domain knowledge isn’t going to help I’m afraid. For those who understand AI, you will be ahead of the curve. My background isn’t in Data Science, so I thought it would be helpful to create a Gen AI – Lexicon for those preparing for the exam.
If you don’t know yet, the difference between prompt engineering or soft prompting, Top K vs Top P, T-Few training and Presence Penalty, then these are just some of the concepts you need to understand. While the knowledge domain is very different for your day-to-day life, it is very interesting! Below are some keywords that will be vital for you to know - look out for them in the material. I’ve deliberately not put the descriptions of what they mean as that would detract from the fun of finding out… happy learning.
- Pipe |
- LLMChain
- PyCharm
- Prompt Injecting
- Chain of Thought Prompting
- Least to Most Prompting
- Step Back Prompting
- K-Shot Prompting
- Zero-Shot Prompting
- Prompt Engineering
- Pre-Training
- T-Few
- Greedy Decoding
- RAG
- Presence penalty
- Soft Prompting
- Fine-Tuning
- Parameter Efficient Fine-Tuning
- Accuracy Measure
- Prompt Templates
- Diffusion Models
- Groundedness
- Answer Relevance
- Cosine Distance of 0
- Accuracy in Vector Databases
- LangChain
- RAG Sequence Model
- StreamLitChatMessages
- Python str.format
- Loss Measure
- Retrievers
- Term Frequency
- Term Frequency Inverse Document Frequency
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