𝐇𝐨𝐰 𝐭𝐨 𝐁𝐞𝐠𝐢𝐧 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬
🔹 𝐋𝐞𝐯𝐞𝐥 𝟏: 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐆𝐞𝐧𝐀𝐈 𝐚𝐧𝐝 𝐑𝐀𝐆
▪️ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development.
▪️ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications.
▪️ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs.
▪️ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks.
🔹 𝐋𝐞𝐯𝐞𝐥 𝟐: 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐢𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬
▪️ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications.
▪️ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs.
▪️ Introduction to AI Agents: Get an overview of AI agents—autonomous entities that use AI to perform tasks or solve problems.
▪️ Agentic Frameworks: Explore popular tools like LangChain or OpenAI’s API to build and manage AI agents.
▪️ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent.
▪️ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops.
▪️ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency.
▪️ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents.
▪️ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently.
▪️ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively.
Join for more AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
🔹 𝐋𝐞𝐯𝐞𝐥 𝟏: 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐆𝐞𝐧𝐀𝐈 𝐚𝐧𝐝 𝐑𝐀𝐆
▪️ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development.
▪️ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications.
▪️ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs.
▪️ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks.
🔹 𝐋𝐞𝐯𝐞𝐥 𝟐: 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐢𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬
▪️ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications.
▪️ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs.
▪️ Introduction to AI Agents: Get an overview of AI agents—autonomous entities that use AI to perform tasks or solve problems.
▪️ Agentic Frameworks: Explore popular tools like LangChain or OpenAI’s API to build and manage AI agents.
▪️ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent.
▪️ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops.
▪️ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency.
▪️ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents.
▪️ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently.
▪️ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively.
Join for more AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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Guys, this post is a must-read if you're even remotely curious about Generative AI & LLMs!
(Save it. Share it)
TOP 10 CONCEPTS YOU CAN'T IGNORE IN GENERATIVE AI
*1. Transformers – The Magic Behind GPT*
Forget the robots. These are the real transformers behind ChatGPT, Bard, Claude, etc. They process all the text at once (not step-by-step like RNNs) making them super smart and insanely fast.
*2. Self-Attention – The Eye of the Model*
This is how the model pays attention to every word while generating output. Like how you remember both the first and last scene of a movie — self-attention lets AI weigh every word’s importance.
*3. Tokenization – Breaking It Down*
AI doesn’t read like us. It breaks sentences into tokens (words or subwords). Even “unbelievable” gets split as “un + believ + able” – that’s why LLMs handle language so smartly.
*4. Pretraining vs Fine-tuning*
Pretraining = Learn everything from scratch (like reading the entire internet).
Fine-tuning = Special coaching (like teaching GPT how to write code, summarize news, or mimic Shakespeare).
*5. Prompt Engineering – Talking to AI in Its Language*
A good prompt = better response. It’s like giving AI the right context or setting the stage properly. One word can change everything. Literally.
*6. Zero-shot, One-shot, Few-shot Learning*
Zero-shot: Model does it with no examples.
One/Few-shot: Model sees 1-2 examples and gets the hang of it.
Think of it like showing your friend how to do a dance step once, and boom—they nail it.
Here you can find more explanation on prompting techniques
👇👇
https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
*7. Diffusion Models – The Art Geniuses*
Behind tools like MidJourney and DALL·E. They work by turning noise into beauty—literally. First they add noise, then learn to reverse it to generate images.
*8. Reinforcement Learning from Human Feedback (RLHF)*
AI gets better with feedback. This is the secret sauce behind making models like ChatGPT behave well (and not go rogue).
*9. Hallucinations – AI's Confident Lies*
Yes, AI can make things up and sound 100% sure. That’s called a hallucination. Knowing when it’s real vs fake is key.
*10. Multimodal Models*
These are the models that don’t just understand text but also images, videos, and audio. Think GPT-4 Vision or Gemini. The future is not just text — it’s everything together.
Generative AI is not just buzz. It's the backbone of a new era.
Credits: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
(Save it. Share it)
TOP 10 CONCEPTS YOU CAN'T IGNORE IN GENERATIVE AI
*1. Transformers – The Magic Behind GPT*
Forget the robots. These are the real transformers behind ChatGPT, Bard, Claude, etc. They process all the text at once (not step-by-step like RNNs) making them super smart and insanely fast.
*2. Self-Attention – The Eye of the Model*
This is how the model pays attention to every word while generating output. Like how you remember both the first and last scene of a movie — self-attention lets AI weigh every word’s importance.
*3. Tokenization – Breaking It Down*
AI doesn’t read like us. It breaks sentences into tokens (words or subwords). Even “unbelievable” gets split as “un + believ + able” – that’s why LLMs handle language so smartly.
*4. Pretraining vs Fine-tuning*
Pretraining = Learn everything from scratch (like reading the entire internet).
Fine-tuning = Special coaching (like teaching GPT how to write code, summarize news, or mimic Shakespeare).
*5. Prompt Engineering – Talking to AI in Its Language*
A good prompt = better response. It’s like giving AI the right context or setting the stage properly. One word can change everything. Literally.
*6. Zero-shot, One-shot, Few-shot Learning*
Zero-shot: Model does it with no examples.
One/Few-shot: Model sees 1-2 examples and gets the hang of it.
Think of it like showing your friend how to do a dance step once, and boom—they nail it.
Here you can find more explanation on prompting techniques
👇👇
https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
*7. Diffusion Models – The Art Geniuses*
Behind tools like MidJourney and DALL·E. They work by turning noise into beauty—literally. First they add noise, then learn to reverse it to generate images.
*8. Reinforcement Learning from Human Feedback (RLHF)*
AI gets better with feedback. This is the secret sauce behind making models like ChatGPT behave well (and not go rogue).
*9. Hallucinations – AI's Confident Lies*
Yes, AI can make things up and sound 100% sure. That’s called a hallucination. Knowing when it’s real vs fake is key.
*10. Multimodal Models*
These are the models that don’t just understand text but also images, videos, and audio. Think GPT-4 Vision or Gemini. The future is not just text — it’s everything together.
Generative AI is not just buzz. It's the backbone of a new era.
Credits: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
❤2👍2
Generative AI
Guys, this post is a must-read if you're even remotely curious about Generative AI & LLMs! (Save it. Share it) TOP 10 CONCEPTS YOU CAN'T IGNORE IN GENERATIVE AI *1. Transformers – The Magic Behind GPT* Forget the robots. These are the real transformers…
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.
Credits: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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.
Credits: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
❤6👍2
David Baum - Generative AI and LLMs for Dummies (2024).pdf
1.9 MB
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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