David Baum - Generative AI and LLMs for Dummies (2024).pdf
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Generative AI and LLMs for Dummies
David Baum, 2024
David Baum, 2024
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Here are 8 concise tips to help you ace a technical AI engineering interview:
𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗟𝗟𝗠 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.
𝟮. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.
𝟯. 𝗦𝗵𝗮𝗿𝗲 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.
𝟰. 𝗦𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.
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.
𝟳. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.
𝟴. 𝗔𝘀𝗸 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗟𝗟𝗠 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.
𝟮. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.
𝟯. 𝗦𝗵𝗮𝗿𝗲 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.
𝟰. 𝗦𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.
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.
𝟳. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.
𝟴. 𝗔𝘀𝗸 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
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LLM Cheatsheet
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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