Generative AI: Developer's Reference

From LLM fundamentals to production-grade AI systems. Built for interview prep and quick refreshers.

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What's Covered

Topic	Key Concepts
LLM Fundamentals	Tokens, Context Window, Temperature, Embeddings, Hallucination
Prompt Engineering	Zero-shot, Few-shot, CoT, ReAct, System Prompts, Injection
RAG	Indexing, Chunking, Vector DBs, Retrieval, Re-ranking
LangChain	LCEL, Prompt Templates, Chains, Memory, Tools, Agents
LangGraph	StateGraph, Nodes, Edges, Loops, Supervisor Pattern
LangSmith	Tracing, Evaluation, Datasets, Production Monitoring

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The Modern AI Development Stack

┌──────────────────────────────────────────────────────────┐
│                   Your Application                        │
└──────────────────────┬───────────────────────────────────┘
                       │
┌──────────────────────▼───────────────────────────────────┐
│              Orchestration Layer                          │
│   LangChain (abstractions) + LangGraph (control flow)    │
└──────┬────────────────────────────┬──────────────────────┘
       │                            │
┌──────▼──────┐             ┌───────▼──────┐
│  LLM APIs   │             │  Knowledge   │
│  GPT-4      │             │  RAG + Vector│
│  Claude     │             │  Databases   │
│  Gemini     │             └──────────────┘
└─────────────┘
       │
┌──────▼──────────────────────────────────────────────────┐
│              Observability: LangSmith                    │
│         Traces, Evaluations, Datasets, Alerts            │
└─────────────────────────────────────────────────────────┘

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The Landscape Mindmap

mindmap
  root((Generative AI))
    Foundation
      Large Language Models
        Transformers
        Tokens
        Context Window
        Embeddings
      Inference Parameters
        Temperature
        Top-p Top-k
        Max tokens
    Prompting
      Zero-shot
      Few-shot
      Chain-of-Thought
      ReAct
      System Prompts
      Prompt Injection Attacks
    Retrieval Augmented Generation
      Document Loading
      Chunking Strategies
      Embedding Models
      Vector Databases
        Pinecone Weaviate Chroma
      Retrieval Strategies
      Re-ranking
    Frameworks
      LangChain
        LCEL Pipe Syntax
        Prompt Templates
        Chains
        Memory
        Tools
        Agents
      LangGraph
        StateGraph
        Nodes and Edges
        Loops and Branching
        Multi-agent patterns
      LangSmith
        Tracing
        Evaluation
        Datasets
        Monitoring

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AI Development Shift

Old way:          Craft a prompt → call API → done

Modern way:       Design systems:
                    - How does the model access current information? (RAG)
                    - What tools can it call?              (Tool use)
                    - How does it handle multi-step tasks? (Agents)
                    - How do you debug when it's wrong?    (Observability)
                    - How do you know it's improving?      (Evaluation)

Simple one-shot LLM calls don't need a framework. LangChain, LangGraph, and LangSmith earn their complexity when you're building pipelines, agents, and production systems that need to be debugged, evaluated, and maintained.