@sairahul1: AI Engineer ┃ ┣ Introduction ┃ ┣ What is an AI Engineer? ┃ ┣ Roles & Responsibilities ┃ ┣ Product Development Impact ┃ …

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A comprehensive thread from @sairahul1 outlining the curriculum for AI engineers, covering LLM fundamentals, prompt engineering, RAG, AI agents, MCP, safety, and more.

AI Engineer ┃ ┣ Introduction ┃ ┣ What is an AI Engineer? ┃ ┣ Roles & Responsibilities ┃ ┣ Product Development Impact ┃ ┣ AI Engineer vs ML Engineer ┃ ┗ Career Paths ┃ ┣ LLM Fundamentals ┃ ┣ AI vs AGI ┃ ┣ Large Language Models (LLMs) ┃ ┣ Tokens ┃ ┣ Context Windows ┃ ┣ Training ┃ ┣ Inference ┃ ┣ Embeddings ┃ ┣ Vector Databases ┃ ┣ Fine-Tuning ┃ ┗ AI Agents ┃ ┣ How LLMs Work ┃ ┣ Tokenization ┃ ┣ Attention Mechanism ┃ ┣ Sampling ┃ ┣ Temperature ┃ ┣ Top-K ┃ ┣ Top-P ┃ ┣ Repetition Penalties ┃ ┗ Generation Process ┃ ┣ Prompt Engineering ┃ ┣ Zero-Shot Prompting ┃ ┣ Few-Shot Prompting ┃ ┣ Chain of Thought ┃ ┣ ReAct Prompting ┃ ┣ Function Calling ┃ ┣ System Prompting ┃ ┣ Prompt Caching ┃ ┣ Structured Outputs ┃ ┣ Prompt Templates ┃ ┗ Prompt Evaluation ┃ ┣ Context Engineering ┃ ┣ External Memory ┃ ┣ Context Composition ┃ ┣ Dynamic Retrieval ┃ ┣ Context Isolation ┃ ┣ Knowledge Injection ┃ ┣ Long-Term Memory ┃ ┣ Session Memory ┃ ┗ Agent Memory Design ┃ ┣ AI Models ┃ ┣ Closed Models ┃ ┃ ┣ OpenAI ┃ ┃ ┣ Anthropic Claude ┃ ┃ ┣ Google Gemini ┃ ┃ ┣ Cohere ┃ ┃ ┗ Mistral ┃ ┃ ┃ ┣ Open Models ┃ ┃ ┣ Llama ┃ ┃ ┣ DeepSeek ┃ ┃ ┣ Qwen ┃ ┃ ┣ Gemma ┃ ┃ ┗ Phi ┃ ┃ ┃ ┣ Hosted Models ┃ ┣ Self Hosted Models ┃ ┣ Quantization ┃ ┣ Fine-Tuning ┃ ┗ Model Evaluation ┃ ┣ Model Selection ┃ ┣ Latency ┃ ┣ Cost ┃ ┣ Quality ┃ ┣ Context Length ┃ ┣ Tool Usage ┃ ┣ Reasoning Capability ┃ ┣ Multimodal Support ┃ ┗ Benchmark Comparison ┃ ┣ Platforms & Ecosystem ┃ ┣ Hugging Face ┃ ┣ Ollama ┃ ┣ LM Studio ┃ ┣ OpenRouter ┃ ┣ Together AI ┃ ┣ Groq ┃ ┣ Fireworks AI ┃ ┗ Vercel AI ┃ ┣ APIs & SDKs ┃ ┣ OpenAI API ┃ ┣ Claude API ┃ ┣ Gemini API ┃ ┣ OpenAI Compatible APIs ┃ ┣ Hugging Face SDK ┃ ┣ LangChain SDK ┃ ┣ LlamaIndex SDK ┃ ┗ AI Gateway Design ┃ ┣ Embeddings ┃ ┣ What are Embeddings? ┃ ┣ Semantic Search ┃ ┣ Classification ┃ ┣ Recommendations ┃ ┣ Similarity Matching ┃ ┣ Clustering ┃ ┣ Anomaly Detection ┃ ┗ Embedding Evaluation ┃ ┣ Embedding Models ┃ ┣ OpenAI Embeddings ┃ ┣ Gemini Embeddings ┃ ┣ Cohere Embeddings ┃ ┣ Sentence Transformers ┃ ┣ Jina Embeddings ┃ ┗ BGE Models ┃ ┣ Vector Databases ┃ ┣ Pinecone ┃ ┣ Weaviate ┃ ┣ Qdrant ┃ ┣ Chroma ┃ ┣ LanceDB ┃ ┣ FAISS ┃ ┣ MongoDB Atlas ┃ ┣ Supabase ┃ ┣ Indexing ┃ ┣ Chunking ┃ ┗ Similarity Search ┃ ┣ Retrieval-Augmented Generation (RAG) ┃ ┣ What is RAG? ┃ ┣ Chunking Strategies ┃ ┣ Embeddings ┃ ┣ Vector Search ┃ ┣ Retrieval ┃ ┣ Re-ranking ┃ ┣ Generation ┃ ┣ RAG vs Fine-Tuning ┃ ┣ Hybrid Search ┃ ┗ Production RAG ┃ ┣ RAG Frameworks ┃ ┣ LangChain ┃ ┣ LlamaIndex ┃ ┣ Haystack ┃ ┣ RAGFlow ┃ ┣ DSPy ┃ ┗ Custom RAG Pipelines ┃ ┣ AI Agents ┃ ┣ Agent Fundamentals ┃ ┣ Agent Use Cases ┃ ┣ Tool Calling ┃ ┣ ReAct ┃ ┣ Planning ┃ ┣ Reflection ┃ ┣ Memory ┃ ┣ Multi-Agent Systems ┃ ┣ Autonomous Workflows ┃ ┗ Agent Architectures ┃ ┣ Agent Frameworks ┃ ┣ OpenAI Agents SDK ┃ ┣ Claude Agent SDK ┃ ┣ Google ADK ┃ ┣ Vercel AI SDK ┃ ┣ CrewAI ┃ ┣ AutoGen ┃ ┣ LangGraph ┃ ┗ Mastra ┃ ┣ Model Context Protocol (MCP) ┃ ┣ MCP Host ┃ ┣ MCP Client ┃ ┣ MCP Server ┃ ┣ Transport Layer ┃ ┣ Data Layer ┃ ┣ Local MCP Servers ┃ ┣ Remote MCP Servers ┃ ┣ Building MCP Tools ┃ ┗ MCP Security ┃ ┣ AI Safety ┃ ┣ Prompt Injection ┃ ┣ Jailbreak Attacks ┃ ┣ Privacy & Security ┃ ┣ Bias & Fairness ┃ ┣ Hallucinations ┃ ┣ Adversarial Testing ┃ ┣ Guardrails ┃ ┣ Output Validation ┃ ┗ Responsible AI ┃ ┣ Observability ┃ ┣ Logging ┃ ┣ Tracing ┃ ┣ Cost Monitoring ┃ ┣ Latency Monitoring ┃ ┣ Production Monitoring ┃ ┣ Langfuse ┃ ┣ LangSmith ┃ ┣ Helicone ┃ ┗ Arize AI ┃ ┣ Evaluations ┃ ┣ Deterministic Evals ┃ ┣ Model-Based Evals ┃ ┣ Human Evals ┃ ┣ Regression Testing ┃ ┣ Benchmarking ┃ ┣ DeepEval ┃ ┣ RAGAS ┃ ┗ Continuous Evaluation ┃ ┣ Multimodal AI ┃ ┣ Image Understanding ┃ ┣ Image Generation ┃ ┣ Video Understanding ┃ ┣ Video Generation ┃ ┣ Audio Processing ┃ ┣ Speech-to-Text ┃ ┣ Text-to-Speech ┃ ┗ Vision Agents ┃ ┣ Multimodal APIs ┃ ┣ OpenAI Vision ┃ ┣ Gemini Vision ┃ ┣ Whisper ┃ ┣ ElevenLabs ┃ ┣ NanoBanana ┃ ┣ Flux ┃ ┣ Veo ┃ ┗ Kling ┃ ┣ AI Development Tools ┃ ┣ Claude Code ┃ ┣ Cursor ┃ ┣ Windsurf ┃ ┣ Gemini CLI ┃ ┣ Codex ┃ ┣ Replit ┃ ┣ Bolt ┃ ┣ Lovable ┃ ┗ Vibe Coding ┃ ┣ Production AI Systems ┃ ┣ Authentication ┃ ┣ Rate Limiting ┃ ┣ Cost Control ┃ ┣ Caching ┃ ┣ Queues ┃ ┣ Background Jobs ┃ ┣ Scaling ┃ ┣ Reliability ┃ ┗ Incident Response ┃ ┗ AI Career Growth ┣ Portfolio Projects ┣ Open Source Contributions ┣ Technical Writing ┣ Interview Preparation ┣ AI Engineering Roadmaps ┣ Freelancing ┣ Startup Building ┣ Consulting ┗ AI Leadership
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Cached at: 07/02/26, 08:20 AM

AI Engineer ┃ ┣ Introduction ┃ ┣ What is an AI Engineer? ┃ ┣ Roles & Responsibilities ┃ ┣ Product Development Impact ┃ ┣ AI Engineer vs ML Engineer ┃ ┗ Career Paths ┃ ┣ LLM Fundamentals ┃ ┣ AI vs AGI ┃ ┣ Large Language Models (LLMs) ┃ ┣ Tokens ┃ ┣ Context Windows ┃ ┣ Training ┃ ┣ Inference ┃ ┣ Embeddings ┃ ┣ Vector Databases ┃ ┣ Fine-Tuning ┃ ┗ AI Agents ┃ ┣ How LLMs Work ┃ ┣ Tokenization ┃ ┣ Attention Mechanism ┃ ┣ Sampling ┃ ┣ Temperature ┃ ┣ Top-K ┃ ┣ Top-P ┃ ┣ Repetition Penalties ┃ ┗ Generation Process ┃ ┣ Prompt Engineering ┃ ┣ Zero-Shot Prompting ┃ ┣ Few-Shot Prompting ┃ ┣ Chain of Thought ┃ ┣ ReAct Prompting ┃ ┣ Function Calling ┃ ┣ System Prompting ┃ ┣ Prompt Caching ┃ ┣ Structured Outputs ┃ ┣ Prompt Templates ┃ ┗ Prompt Evaluation ┃ ┣ Context Engineering ┃ ┣ External Memory ┃ ┣ Context Composition ┃ ┣ Dynamic Retrieval ┃ ┣ Context Isolation ┃ ┣ Knowledge Injection ┃ ┣ Long-Term Memory ┃ ┣ Session Memory ┃ ┗ Agent Memory Design ┃ ┣ AI Models ┃ ┣ Closed Models ┃ ┃ ┣ OpenAI ┃ ┃ ┣ Anthropic Claude ┃ ┃ ┣ Google Gemini ┃ ┃ ┣ Cohere ┃ ┃ ┗ Mistral ┃ ┃ ┃ ┣ Open Models ┃ ┃ ┣ Llama ┃ ┃ ┣ DeepSeek ┃ ┃ ┣ Qwen ┃ ┃ ┣ Gemma ┃ ┃ ┗ Phi ┃ ┃ ┃ ┣ Hosted Models ┃ ┣ Self Hosted Models ┃ ┣ Quantization ┃ ┣ Fine-Tuning ┃ ┗ Model Evaluation ┃ ┣ Model Selection ┃ ┣ Latency ┃ ┣ Cost ┃ ┣ Quality ┃ ┣ Context Length ┃ ┣ Tool Usage ┃ ┣ Reasoning Capability ┃ ┣ Multimodal Support ┃ ┗ Benchmark Comparison ┃ ┣ Platforms & Ecosystem ┃ ┣ Hugging Face ┃ ┣ Ollama ┃ ┣ LM Studio ┃ ┣ OpenRouter ┃ ┣ Together AI ┃ ┣ Groq ┃ ┣ Fireworks AI ┃ ┗ Vercel AI ┃ ┣ APIs & SDKs ┃ ┣ OpenAI API ┃ ┣ Claude API ┃ ┣ Gemini API ┃ ┣ OpenAI Compatible APIs ┃ ┣ Hugging Face SDK ┃ ┣ LangChain SDK ┃ ┣ LlamaIndex SDK ┃ ┗ AI Gateway Design ┃ ┣ Embeddings ┃ ┣ What are Embeddings? ┃ ┣ Semantic Search ┃ ┣ Classification ┃ ┣ Recommendations ┃ ┣ Similarity Matching ┃ ┣ Clustering ┃ ┣ Anomaly Detection ┃ ┗ Embedding Evaluation ┃ ┣ Embedding Models ┃ ┣ OpenAI Embeddings ┃ ┣ Gemini Embeddings ┃ ┣ Cohere Embeddings ┃ ┣ Sentence Transformers ┃ ┣ Jina Embeddings ┃ ┗ BGE Models ┃ ┣ Vector Databases ┃ ┣ Pinecone ┃ ┣ Weaviate ┃ ┣ Qdrant ┃ ┣ Chroma ┃ ┣ LanceDB ┃ ┣ FAISS ┃ ┣ MongoDB Atlas ┃ ┣ Supabase ┃ ┣ Indexing ┃ ┣ Chunking ┃ ┗ Similarity Search ┃ ┣ Retrieval-Augmented Generation (RAG) ┃ ┣ What is RAG? ┃ ┣ Chunking Strategies ┃ ┣ Embeddings ┃ ┣ Vector Search ┃ ┣ Retrieval ┃ ┣ Re-ranking ┃ ┣ Generation ┃ ┣ RAG vs Fine-Tuning ┃ ┣ Hybrid Search ┃ ┗ Production RAG ┃ ┣ RAG Frameworks ┃ ┣ LangChain ┃ ┣ LlamaIndex ┃ ┣ Haystack ┃ ┣ RAGFlow ┃ ┣ DSPy ┃ ┗ Custom RAG Pipelines ┃ ┣ AI Agents ┃ ┣ Agent Fundamentals ┃ ┣ Agent Use Cases ┃ ┣ Tool Calling ┃ ┣ ReAct ┃ ┣ Planning ┃ ┣ Reflection ┃ ┣ Memory ┃ ┣ Multi-Agent Systems ┃ ┣ Autonomous Workflows ┃ ┗ Agent Architectures ┃ ┣ Agent Frameworks ┃ ┣ OpenAI Agents SDK ┃ ┣ Claude Agent SDK ┃ ┣ Google ADK ┃ ┣ Vercel AI SDK ┃ ┣ CrewAI ┃ ┣ AutoGen ┃ ┣ LangGraph ┃ ┗ Mastra ┃ ┣ Model Context Protocol (MCP) ┃ ┣ MCP Host ┃ ┣ MCP Client ┃ ┣ MCP Server ┃ ┣ Transport Layer ┃ ┣ Data Layer ┃ ┣ Local MCP Servers ┃ ┣ Remote MCP Servers ┃ ┣ Building MCP Tools ┃ ┗ MCP Security ┃ ┣ AI Safety ┃ ┣ Prompt Injection ┃ ┣ Jailbreak Attacks ┃ ┣ Privacy & Security ┃ ┣ Bias & Fairness ┃ ┣ Hallucinations ┃ ┣ Adversarial Testing ┃ ┣ Guardrails ┃ ┣ Output Validation ┃ ┗ Responsible AI ┃ ┣ Observability ┃ ┣ Logging ┃ ┣ Tracing ┃ ┣ Cost Monitoring ┃ ┣ Latency Monitoring ┃ ┣ Production Monitoring ┃ ┣ Langfuse ┃ ┣ LangSmith ┃ ┣ Helicone ┃ ┗ Arize AI ┃ ┣ Evaluations ┃ ┣ Deterministic Evals ┃ ┣ Model-Based Evals ┃ ┣ Human Evals ┃ ┣ Regression Testing ┃ ┣ Benchmarking ┃ ┣ DeepEval ┃ ┣ RAGAS ┃ ┗ Continuous Evaluation ┃ ┣ Multimodal AI ┃ ┣ Image Understanding ┃ ┣ Image Generation ┃ ┣ Video Understanding ┃ ┣ Video Generation ┃ ┣ Audio Processing ┃ ┣ Speech-to-Text ┃ ┣ Text-to-Speech ┃ ┗ Vision Agents ┃ ┣ Multimodal APIs ┃ ┣ OpenAI Vision ┃ ┣ Gemini Vision ┃ ┣ Whisper ┃ ┣ ElevenLabs ┃ ┣ NanoBanana ┃ ┣ Flux ┃ ┣ Veo ┃ ┗ Kling ┃ ┣ AI Development Tools ┃ ┣ Claude Code ┃ ┣ Cursor ┃ ┣ Windsurf ┃ ┣ Gemini CLI ┃ ┣ Codex ┃ ┣ Replit ┃ ┣ Bolt ┃ ┣ Lovable ┃ ┗ Vibe Coding ┃ ┣ Production AI Systems ┃ ┣ Authentication ┃ ┣ Rate Limiting ┃ ┣ Cost Control ┃ ┣ Caching ┃ ┣ Queues ┃ ┣ Background Jobs ┃ ┣ Scaling ┃ ┣ Reliability ┃ ┗ Incident Response ┃ ┗ AI Career Growth ┣ Portfolio Projects ┣ Open Source Contributions ┣ Technical Writing ┣ Interview Preparation ┣ AI Engineering Roadmaps ┣ Freelancing ┣ Startup Building ┣ Consulting ┗ AI Leadership

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