If you are working in the space of large language models (LLMs) and agent-based workflows, you've likely encountered both LangChain and LangGraph. They come from the same ecosystem, but they have different design goals, abstractions (level and style of conceptual modeling), and use cases. What is LangChain? LangChain is described as a framework for developing applications powered by large language models (LLMs). It emphasizes chains of components (like model calls, prompt pro

Imagine you could assemble a team of intelligent assistants, each with a unique role, goal, and expertise, and they could collaborate to solve complex tasks automatically. That's exactly what CrewAI enables. In simple terms, CrewAI is a framework (a toolkit) for building teams of AI agents, not just one AI working alone, but many agents working together, each playing a specific role. According to its makers, it is a "lean, lightning-fast Python framework built entirely from s

Imagine a language model like a smart student who has read almost every book in the world. This student knows a lot about general knowledge and language, but sometimes isn’t perfect for a specific task you want them to do. Fine-tuning   is like giving this student   extra lessons on a specific topic : The student already knows general stuff (like grammar, facts, and reasoning). You now give them a smaller set of examples focused on your task (e.g., answering customer service

In recent years,   large language models (LLMs)   like GPT, Claude, and Gemini have become incredibly powerful at generating human-like text. But they still have a problem: they rely only on what they were trained on. This means they can   hallucinate answers , give outdated information, or struggle with highly specialized topics. That’s where   Retrieval-Augmented Generation (RAG)   comes in. What is Retrieval-Augmented Generation (RAG)? RAG is a hybrid approach that combine

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