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aijobsok.Practical AI learning for curious builders
TopicsInterview Questions
  • Overview
  • All AI topics
  • Generative AI Fundamentals
    • What is Generative AI?
    • Transformers Architecture
    • Tokens, Context Windows, and Temperature
    • Prompt Engineering Best Practices
    • Generative AI Use Cases and Limitations
  • Retrieval-Augmented Generation (RAG)
    • Introduction to RAG Architecture
    • Vector Embeddings and Chunking
    • Choosing a Vector Database
    • Building a RAG Pipeline
    • Advanced RAG
  • AI Agents & Orchestration
    • Autonomous AI Agents Explained
    • LangChain vs. LlamaIndex
    • Building Tool-Calling Agents
    • Multi-Agent Systems
    • Agent Memory Management
  • Model Context Protocol (MCP)
    • Introduction to MCP
    • Setting Up an MCP Server
    • Connecting Local Tools via MCP
    • Security and Authentication in MCP
    • Building Custom MCP Clients
  • Fine-Tuning Models
    • Fine-Tuning vs. RAG
    • Supervised Fine-Tuning (SFT)
    • Parameter-Efficient Fine-Tuning (PEFT)
    • Preparing Datasets
    • Fine-Tuning Open Models
  • Local AI & Open-Source Models
    • Running LLMs Locally
    • Quantization Explained
    • LM Studio Integration
    • High-Throughput Serving
    • Evaluating Model Benchmarks
  • Multimodal AI
    • Vision-Language Models (VLMs)
    • Diffusion Models Architecture
    • Audio Processing Pipelines
    • Multimodal RAG
    • Building Multimodal Chatbots
  • LLMOps (MLOps for LLMs)
    • Introduction to LLMOps
    • Prompt Tracking and Versioning
    • Evaluating LLM Outputs
    • API Cost and Latency Management
    • Deploying to Production
  • AI Security & Guardrails
    • Prompt Injection Risks
    • Implementing NeMo Guardrails
    • PII Redaction
    • Toxicity and Hallucination Filtering
    • Compliance for AI Applications
  • Python for AI Development
    • AI Environment Setup
    • API Integrations
    • Structured Outputs
    • Data Processing for AI
    • Rapid UI Prototyping
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