Python
Classes in Python
advanced
__new__ and __init__ play distinct roles in Python object creation. While __new__ is responsible for creating and returning a new instance, __init__ initializes that …
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Python
Metaclasses in Python
advanced
Metaclasses are Python’s mechanism for controlling class creation itself. While classes define how objects are created, metaclasses define how classes are created. In this …
Python
Magic Methods in Python
intermediate
Magic Methods in Python: The Secret Sauce Behind Elegant Code Ever wondered why you can write vector1 + vector2 , if user in users …
Python
Magic Attributes
intermediate
Magic attributes, also known as dunder attributes, are special variables that Python automatically creates and attaches to modules, classes, functions, and objects. They provide …
AI Agents
AI Agent
beginner
AI agents are systems that go beyond answering questions—they can understand goals, make decisions, and take actions autonomously. Powered by models like ChatGPT, they …
AI Agents LangGraph
LangGraph
intermediate
LangGraph Deep Dive Series In this series, we explore how to build powerful, production-ready AI workflows using LangGraph . We’ll go beyond simple prompt …
AI Agents LLM
Large Language Models (LLMs)
beginner
Large Language Models (LLMs) are the core intelligence behind AI agents. They enable agents to understand language, reason through problems, and plan actions. Models …
LangGraph
Graphs
intermediate
In this post, we explored the basics of Graph Theory and discussed the major types of graphs, including directed graphs, cyclic graphs, trees, multigraphs, …
AI Agents LangGraph
LangGraph START/END points
intermediate
In LangGraph, every graph workflow requires a clearly defined starting point and ending point. LangGraph provides two special built-in markers for this purpose: START …
AI Agents LangGraph
Nodes in LangGraph
intermediate
In LangGraph, a node is the fundamental building block of any workflow. It is a reusable unit of logic that receives the current state, …
AI Agents LangGraph
Edges in LangGraph
intermediate
In this topic, we explore edges in LangGraph and understand how they control execution flow between nodes in a graph-based workflow. We discuss why …
AI Agents LangGraph
Conditional Edges in LangGraph
intermediate
In this topic, we explore conditional edges in LangGraph and understand how they enable dynamic and intelligent workflow execution. We discuss the difference between …
AI Agents LangGraph
Cycles and Self-Loops in LangGraph
intermediate
In this topic, we explore cycles and self-loops in LangGraph and understand how they enable iterative, adaptive, and autonomous AI workflows. We discuss the …
AI Agents LangGraph
StateGraph vs MessageGraph
intermediate
In this topic, we explore the differences between StateGraph and MessageGraph in LangGraph, how each graph type works, and when to use them in …
AI Agents LangGraph
Graph Compilation in LangGraph
intermediate
In this topic, we explore graph compilation in LangGraph and understand how a graph is transformed from a builder definition into an executable workflow. …
AI Agents LangGraph
Graph Visualization in LangGraph
intermediate
In this topic, we explore graph visualization in LangGraph and learn how to visually inspect workflow structures, execution flows, nodes, edges, cycles, routing logic, …
AI Agents LangGraph
State in LangGraph
intermediate
This post explores everything about State in LangGraph, how it works as shared memory between nodes, how data flows through graphs, and how state …
AI Agents LangGraph
Reducers
intermediate
This post covers everything you need to know about Reducers in LangGraph — how they manage and merge state updates across nodes, especially during …
AI Agents LangGraph
Messages & Chat History
intermediate
This post explores how messages work in LangGraph , including HumanMessage , AIMessage , SystemMessage , and ToolMessage . We cover storing messages in …
AI Agents LangGraph
System Prompts
intermediate
This post covers everything about System Prompts in LangGraph — how they define AI behavior, roles, and instruction hierarchy. We explore persona design, safety …
AI Agents LangGraph
Prompt Templates
intermediate
This post explores Prompt Templates in LangGraph and LangChain , including static and dynamic templates, template variables, reusable prompt components, and template composition. We …
AI Agents LangGraph
Prompt Chaining
intermediate
This post covers Prompt Chaining in LangGraph , showing how multiple prompts can work together in sequential workflows and multi-step reasoning pipelines. We explore …
AI Agents LangGraph
Streaming
intermediate
This post explores Streaming in LangGraph , including real-time state updates, token streaming, node-level streaming, and streaming LLM or tool outputs. We cover stream() …
AI Agents LangGraph
Interrupts
intermediate
This post covers Interrupts in LangGraph , explaining how to pause and resume graph execution for human-in-the-loop workflows. We explore waiting for approvals, maintaining …
AI Agents LangGraph
Breakpoints
intermediate
This post explains Breakpoints in LangGraph and how they help debug workflows by pausing execution at specific points. It covers inspecting state at runtime, …
AI Agents LangGraph
Retry Logic
intermediate
This post covers Retry Logic in LangGraph , explaining how and why retries are used to handle failures in workflows. It includes automatic and …
AI Agents LangGraph
Model Integration
intermediate
This post covers Model Integration in LangGraph , explaining how LLMs are connected and orchestrated within graph-based workflows. It explores model lifecycle, binding models …
AI Agents LangGraph
Structured Output Prompting
intermediate
This post covers Structured Output Prompting , focusing on how to make LLMs produce reliable, machine-readable outputs. It includes JSON, schema-guided, and Pydantic-based prompting, …
AI Agents LangGraph
Pydantic Outputs
intermediate
This post explains Pydantic Outputs in LangGraph , focusing on how to enforce structured, type-safe LLM responses using Pydantic models. It covers defining structured …
RAG
RAG Systems
intermediate
Retrieval-Augmented Generation (RAG) has become the foundation of nearly every production-grade AI application. Instead of relying solely on an LLM’s internal knowledge, RAG dynamically …
RAG
Embeddings
intermediate
This guide covers embeddings in depth — the foundation of all modern Retrieval-Augmented Generation (RAG) systems and semantic search. You will learn what embeddings …
RAG
Vector Databases
intermediate
This comprehensive guide dives deep into Vector Databases — the backbone of scalable semantic search and Retrieval-Augmented Generation (RAG) systems. You will learn core …
RAG
Retrievers
intermediate
This guide explores Retrievers in depth — the critical component that bridges user queries and relevant context in RAG systems and AI agents. You …
RAG
Document Loaders
intermediate
This guide covers Document Loaders comprehensively — the essential first step in any RAG or AI agent system. You will learn how to load …
RAG
RAG Pipelines
advanced
This guide brings everything together into complete RAG Pipelines — from simple query-answer flows to sophisticated agentic, multi-step, and stateful RAG systems using LangGraph. …
RAG
Hybrid Retrieval
advanced
This guide explores Hybrid Retrieval — one of the most effective techniques to dramatically improve RAG performance. You will learn how to combine semantic …