Have you ever wondered how modern AI applications can handle multiple specialized tasks efficiently? Whether it's customer support, sales analytics, or academic scheduling, today's applications need different AI agents for different problems. That's where LangChain comes in. LangChain is a framework that allows developers to build sophisticated applications with Large Language Models (LLMs) by creating agents that can intelligently use tools, access databases, and make deci

**source: QLoRA Efficient Finetuning of Quantized LLMs 1. Full Fine-Tuning (The "Total Brain Rewrite") In this method, you update the entire AI "brain" (the Base Model) at once. How it works: Every single connection in the model is modified to learn the new task. The cost: Because it uses 32-bit and 16-bit processing for every single part of the model, it requires massive amount of computer memory and power. Analogy: It's like rewriting an entire 500-page textbook just to

A prompt injection attack is when someone gives cleverly crafted input to an AI system that tricks into doing something you didn't intend - like revealing hidden instructions, or doing harmful actions. How to handle prompt injection attacks? Check the user input first (input validation) Before the model sees it, examine what user submit. Look for weird, dangerous, or clearly malicious content. clean it up or block it if you see risky patterns. This stop a lot of attacks befor

Prompt Engineering means carefully designing, improving, and repeating the input you give to a language model so it produces the output you want. Instead of building new models, you guide an existing model by how you ask it. It's a blend of creativity (how you phrase things) and methodical testing (seeing what works best). Moreover, it isn't about clever wording, it's the key to unlocking the full value of large language models by steering them toward accurate, relevant, and

**source: GeeksforGeeks (image) Understanding Large Language Models (LLMs) Large Language Models (LLM) are powerful neural networks, typically comprising billions of parameters, that are trained on huge volumes of text so that they can understand, generate, and reason about human-language content. They mark a major shift on how natural-language processing (NLP) systems are built and deployed. Key Characteristics Scale: LLMs often run into billions of parameters. For example

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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