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9 Key AI Concepts Explained in 7 minutes - Tokenization - Text Decoding - Prompt Engineering - Multi Step AI Agents - RAGs - RLHF - VAE - Diffusion Models - LoRA

Bytebytego

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98,842 次观看 • 6 个月前 •via X (Twitter)

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CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

Dami-Defi

10,127 次观看 • 1 个月前

RIP traditional prompt engineering platforms. Meet Adaline Ai the single platform where you can write, test, and ship the best prompts for your AI Agents, MCP, RAG, and LLM apps. One platform. Zero hassle. Step-by-step tutorial :

RIP traditional prompt engineering platforms. Meet Adaline Ai the single platform where you can write, test, and ship the best prompts for your AI Agents, MCP, RAG, and LLM apps. One platform. Zero hassle. Step-by-step tutorial :

Parul Gautam

43,009 次观看 • 1 年前

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

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Agentic Commerce explained in 5 minutes. Why AI agents will transact on crypto rails, and where LI.FI fits.

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Google just dropped a 1-hour course on how to become an AI engineer in 2026: • 00:00 - Context engineering • 10:00 - How to build, deploy, and run AI agents • 31:52 - Agentic loops, long-running AI agents • 43:22 - Building MCP from scratch • 51:20 - Prompt engineering • 59:10 - AI development trends This 1-hour watch will replace a $500 AI engineering course. Watch it today, then read the full pipeline in the article below.

Google just dropped a 1-hour course on how to become an AI engineer in 2026: • 00:00 - Context engineering • 10:00 - How to build, deploy, and run AI agents • 31:52 - Agentic loops, long-running AI agents • 43:22 - Building MCP from scratch • 51:20 - Prompt engineering • 59:10 - AI development trends This 1-hour watch will replace a $500 AI engineering course. Watch it today, then read the full pipeline in the article below.

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183,577 次观看 • 24 天前

Key takeaways from CZ 🔶 BNB at WEF: • Three growth pillars: tokenization, payments, and AI • Dozens of governments are exploring asset tokenization to unlock liquidity • Crypto is becoming the native payment layer for AI agents • Crypto infrastructure has already handled multi-billion-dollar stress events • One-size-fits-all global crypto rules won’t work; local frameworks matter

Key takeaways from CZ 🔶 BNB at WEF: • Three growth pillars: tokenization, payments, and AI • Dozens of governments are exploring asset tokenization to unlock liquidity • Crypto is becoming the native payment layer for AI agents • Crypto infrastructure has already handled multi-billion-dollar stress events • One-size-fits-all global crypto rules won’t work; local frameworks matter

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54,796 次观看 • 6 个月前

i decided to put together all my AI engineering posts in a single pdf. it covers: > LLM foundations > prompt engineering > fine-tuning > RAG > context engineering > AI agents > MCP > optimization > deployment > eval and observability 375+ pages. download link in next tweet!

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🎬 NOW LIVE: "The AI Research Lab - Explained" debuts with our groundbreaking work on Large Action Models! Watch now: Watch as Shelby Heinecke Shelby Heinecke reveals how we're training these specialized models to generate precise, executable actions rather than merely text—enabling AI agents to respond in less than a second. #LAMs #EnterpriseAI

🎬 NOW LIVE: "The AI Research Lab - Explained" debuts with our groundbreaking work on Large Action Models! Watch now: Watch as Shelby Heinecke Shelby Heinecke reveals how we're training these specialized models to generate precise, executable actions rather than merely text—enabling AI agents to respond in less than a second. #LAMs #EnterpriseAI

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🚨 AI Can Build Complex Apps With Just One Single Prompt New AI models may be banned but multi-LLM agents can build insane things.... Here is our agent building an entire Slack-like app using a single prompt! Combines Opus, GPT 5.5 and open-source LLMs to optimize cost and performance

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This video by explains the fundamentals of context engineering in just 14 minutes. Context Engineering is effectively the #1 job of engineers building AI agents. do not sleep on it.

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AI PM jobs pay $250K+. Here's your roadmap to break in, from the guy who teaches AI PM at Stanford, UCLA, and Maven: 1:21 - AI PM Salaries 9:13 - Live Demo: Agent + RAG 38:48 - Context Engineering vs Prompt Engineering 43:28 - Complete Roadmap: Zero to AI PM at Top Companies

AI PM jobs pay $250K+. Here's your roadmap to break in, from the guy who teaches AI PM at Stanford, UCLA, and Maven: 1:21 - AI PM Salaries 9:13 - Live Demo: Agent + RAG 38:48 - Context Engineering vs Prompt Engineering 43:28 - Complete Roadmap: Zero to AI PM at Top Companies

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AI answers your questions in seconds, but behind that speed is something called inference—the compute-intensive process where trained models generate responses. At AWS, we've built custom chips like Trainium, intelligent routing systems, and unified infrastructure to make inference faster and more affordable. As AI agents handle complex multi-step tasks, inference accounts for 80-90% of AI computing power. We're engineering at planetary scale to keep those milliseconds reliable.

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Despite constant improvements, AI agents still struggle with complex, multi-step tasks. @Gulp_AI (YC W25) solves this with Osmosis: a framework that helps AI agents learn (similar to DeepSeek R1) in real time. Congrats on the launch Kasey Zhang + Andy!

Despite constant improvements, AI agents still struggle with complex, multi-step tasks. @Gulp_AI (YC W25) solves this with Osmosis: a framework that helps AI agents learn (similar to DeepSeek R1) in real time. Congrats on the launch Kasey Zhang + Andy!

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Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

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Don't waste 2 years learning to build self-improving AI agents. Stanford just dropped a 3 hour course on building AI agents from scratch. • 00:00 - self-improving AI agent basics • 1:07:24 - multi-step reasoning in AI agents • 2:22:04 - AI agents learning from feedback Anthropic pays $750,000/year to AI engineers who master this exact knowledge of building AI agents. Bookmark this & watch, no matter what. It’ll be the most productive start for your week. Then read article below.

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The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!

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Today we’re sharing two new advances in our generative AI research: Emu Video & Emu Edit. Details ➡️ These new models deliver exciting results in high quality, diffusion-based text-to-video generation & controlled image editing w/ text instructions. 🧵

Today we’re sharing two new advances in our generative AI research: Emu Video & Emu Edit. Details ➡️ These new models deliver exciting results in high quality, diffusion-based text-to-video generation & controlled image editing w/ text instructions. 🧵

AI at Meta

798,304 次观看 • 2 年前

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

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151,198 次观看 • 2 年前