Guys, here are 10 more next-level Generative AI terms that’ll make you sound like you’ve been working at OpenAI (even if you're just exploring)!

TOP 10 ADVANCED TERMS IN GENERATIVE AI (Vol. 2)

*1. LoRA (Low-Rank Adaptation)*

Tiny brain upgrades for big models. LoRA lets you fine-tune huge LLMs without burning your laptop. It’s like customizing ChatGPT to think like you — but in minutes.


*2. Embeddings*

This is how AI understands meaning. Every word or sentence becomes a string of numbers (vectors) in a high-dimensional space — so "king" and "queen" end up close to each other.


*3. Context Window*

It’s like the memory span of the model. GPT-3.5 has ~4K tokens. GPT-4 Turbo? 128K tokens. More tokens = model remembers more of your prompt, better answers, fewer “forgot what you said” moments.


*4. Retrieval-Augmented Generation (RAG)*

Want ChatGPT to know your documents or PDFs? RAG does that. It mixes search with generation. Perfect for building custom bots or AI assistants.


*5. Instruction Tuning*

Ever noticed how GPT-4 just knows how to follow instructions better? That’s because it’s been trained on instruction-style prompts — "summarize this", "translate that", etc.


*6. Chain of Thought (CoT) Prompting*

Tell AI to think step by step — and it will!

CoT prompting boosts reasoning and math skills. Just add “Let’s think step-by-step” and watch the magic.


*7. Fine-tuning vs. Prompt-tuning*

- Fine-tuning: Teach the model new behavior permanently.

- Prompt-tuning: Use clever inputs to guide responses without retraining.

You can think of it as permanent tattoo vs. temporary sticker. 😅



*8. Latent Space*

This is where creativity happens. Whether generating text, images, or music — AI dreams in latent space before showing you the result.


*9. Diffusion vs GANs*

- Diffusion = controlled chaos (used by DALL·E 3, MidJourney)

- GANs = two AIs fighting — one generates, one critiques

Both create stunning visuals, but Diffusion is currently winning the art game.



*10. Agents / Auto-GPT / BabyAGI*

These are like AI with goals. They don’t just respond — they act, search, loop, and try to accomplish tasks. Think of it like ChatGPT that books your flight and packs your bag.

React with ❤️ if it helps

If you understand even 5 of these terms, you're already ahead of 95% of the crowd.

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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.

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Inside Generative AI
Rick Spair, 2024

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.

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