AI discussions¶

The goal of this section is to get a set of content to support deeper discussions around Gen AI, during chit-chat or interviews.

1. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗟𝗟𝗠 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀¶

Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.

• General LLM introduction
• Transformer and GPT-3 summary
• How LLM pre-training is done
• How to fine tune existing model
• How RAG works

𝟮. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴¶

Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.

• Prompt Engineering
• Demonstrate prompt engineering
• How to optimize model response performance with prompt

𝟯. 𝗦𝗵𝗮𝗿𝗲 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀¶

Walk through hands-on experiences leveraging models like GPT-3, Langchain, or Vector Databases.

• Review RAG positioning, architecture
• Streamlit app to demonstrate RAG with Chromadb. Offline tool to create vector store and indexing build_agent_domain_rag.py using a Lilian Weng's multi-agents blog.
• Multiple queries RAG with LangChain

𝟰. 𝗦𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵¶

Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.

• few-shot learning
• chain of thought
• Agentic

𝟱. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀¶

Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.

𝟲. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀¶

Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.

• supervised fine-tuning

𝟳. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲¶

• From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale, and monitoring model inference.