Generative AI (GenAI) is the branch of artificial intelligence focused on creating new content text, code, images, audio, video, and structured data by learning patterns from existing examples. In 2026, it’s no longer experimental: enterprises use GenAI for coding assistance, customer support, content creation, knowledge search, and workflow automation.
At Varsity by InterviewBit, this category brings together courses, tutorials, and in‑depth articles that help developers, data professionals, and product leaders move from “playing with chatbots” to building production-grade GenAI applications and leading AI initiatives inside their organizations.
The best gen ai projects in 2026 are judged by the evidence they produce, not the idea behind them. A document chatbot proves nothing, because anyone can build one in an afternoon. The same project with a 40-example golden dataset,…
Among agentic AI frameworks in 2026, LangGraph is the default for stateful production workflows that need checkpointing, retries and human approval. CrewAI is the fastest route to a working multi-agent prototype. AutoGen has been superseded by the Microsoft Agent Framework,…
A gen ai roadmap for a working software engineer in 2026 has six stages: LLM APIs and structured outputs, evaluation, retrieval augmented generation, tool calling and agents, production concerns such as tracing and cost, and fine-tuning last. It takes three…
In the Cursor vs Claude Code comparison, Cursor is an AI-native code editor built around staying in the loop, and Claude Code is a terminal and IDE agent built around delegating whole tasks. Cursor wins when you are reading, steering…
In the SLM vs LLM comparison, a small language model is any model small enough to run on a single consumer device, which in practice means under roughly 10 billion parameters, while a large language model needs cloud infrastructure. SLMs…
LangChain is an open-source framework for building applications on top of large language models. It gives you a standard interface to models, tools, retrieval and memory, so you can swap providers without rewriting your application, plus a configurable agent harness…
LangGraph is a low-level orchestration framework and runtime for building stateful, long-running AI agents. You model your application as a graph: a typed state object, nodes that read and update it, and edges that decide what runs next. Its defining…
The most common gen ai interview questions cover seven areas: transformer and attention mechanics, RAG versus fine-tuning, chunking and embedding selection, hallucination mitigation, evaluation methodology, agent and tool design, and cost or latency optimisation. RAG and evaluation together make up…
Learn LangChain first. They are not competitors: LangChain is the agent and component layer, LangGraph is the stateful runtime it runs on. Start with LangChain’s create_agent, then drop to LangGraph when you need custom loops, durable state or human approval…
The difference in one line: generative AI creates content in response to a prompt, while agentic AI pursues a goal by planning, using tools and acting across systems with limited human input. Generative AI produces an output for a human…
