AI for Software Engineers: LLMs, RAG & Agents Explained Simply (No Hype)

Описание: Are you a software engineer who keeps hearing "AI", "LLMs", "RAG", "Agents" — and nodding along without really knowing what any of it means? This session is for you.

In this recorded session, we go from confusion to clarity — no math, no hype, no PhD required. Just honest, engineer-friendly explanations of the technology that's reshaping our industry.

What you'll learn:
→ How AI, ML, Deep Learning, and GenAI actually connect (you already know more than you think)
→ What an LLM is and how it really works (it's a very powerful autocomplete)
→ Prompt engineering — what actually matters and why tokens = cost
→ How to call an AI API as a developer (it's just a POST request)
→ Why LLMs have limitations — knowledge cutoff, hallucinations, no private data
→ RAG explained with the open-book exam analogy — plug your own data into any LLM
→ Agents — the human analogy: Brain = LLM, Hands = Tools, Memory, Loop
→ Multi-agent systems with crewAI — real stock trading example
→ Where to start learning (and what to skip first)

🛠 Resources mentioned:
→ Hugging Face Spaces (try live AI demos, no login): https://huggingface.co/spaces
→ DeepLearning.AI free short courses: https://www.deeplearning.ai/short-courses/
→ OpenAI API: https://platform.openai.com
→ Anthropic API (Claude): https://console.anthropic.com
→ crewAI quickstart: https://docs.crewai.com
→ Andrej Karpathy — "Neural Networks: Zero to Hero" (YouTube): search on YouTube

📌 This is Session 1 of an ongoing AI series for software engineers. More sessions coming on RAG hands-on, building agents from scratch, and production AI systems.

If this helped you — like the video and drop a comment with the one thing that clicked for you. It genuinely helps the channel and tells me what to cover next.

#AI #MachineLearning #GenerativeAI #LLM #RAG #AIAgents #SoftwareEngineering #ChatGPT #ArtificialIntelligence #LearningAI