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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

136,250 subscribers

99,854 次观看 • 7 个月前 •via X (Twitter)

教育科学技术

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12 条评论

墨白的头像

墨白7 个月前

what tools can generate the animation like this

Tech Bro 的头像

Tech Bro7 个月前

Solid stack. If you aren't optimizing your LoRA adapters for specific agentic tasks yet, you're leaving 10x performance on the table. Fundamentals matter.

Joshua Poddoku 的头像

Joshua Poddoku7 个月前

RAGs rely more on data hygiene than clever prompts

CodeToCompass 的头像

CodeToCompass7 个月前

Very easily explained

Nyx 的头像

Nyx7 个月前

Multi-step agents are powerful but the attack surface grows with each step. Has anyone documented which step in the RAG pipeline is most vulnerable to prompt injection? Feels like everyone's racing to add steps without hardening the basics.

google fu 的头像

google fu7 个月前

how to make the video by tools ?Could you give some recommendations?

toni 的头像

toni7 个月前

Everyone learns these 9 concepts separately. The real skill is combining 3-4 of them into one system. Knowing the parts!= building the machine. Integration is the actual hard part nobody covers in 7 minutes.

Himanshu Kumar 的头像

Himanshu Kumar7 个月前

@bytebytego, understanding these AI concepts is crucial for effective prompt engineering; a good resource will help.

Gregor 的头像

Gregor7 个月前

What's the biggest challenge in implementing these AI concepts in a way that produces actionable results for businesses? I've seen companies struggle to apply theoretical knowledge to real-world problems.

patriotic11 的头像

patriotic117 个月前

Great 👏👏

harjot.co 的头像

harjot.co7 个月前

Space-saver tip: 'LLM' for all!

GlitchPool 的头像

GlitchPool7 个月前

these 7min explainers are clutch for understanding how stuff actually works under the hood. RAG and multi-step agents especially useful rn

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A team of researchers from ByteDance has introduced MVDream, a multi-view diffusion model capable of generating 3D models from text prompts. Learn more: #mvdream #research #researchpaper #diffusion #diffusionmodel #ai #aitech #aitechnology #textto3d

A team of researchers from ByteDance has introduced MVDream, a multi-view diffusion model capable of generating 3D models from text prompts. Learn more: #mvdream #research #researchpaper #diffusion #diffusionmodel #ai #aitech #aitechnology #textto3d

80 LEVEL

724,033 次观看 • 3 年前

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.

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Dami-Defi

10,127 次观看 • 3 个月前

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

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188,973 次观看 • 2 个月前

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