Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a technique that enhances language model responses by first retrieving relevant documents from a knowledge base. RAG reduces hallucinations and keeps AI responses grounded in up-to-date, factual information.
Understanding Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an architecture pattern that enhances large language model outputs by retrieving relevant documents from an external knowledge base and incorporating them into the generation context. Rather than relying solely on the model's parametric knowledge learned during pre-training, RAG systems first use embedding-based similarity search or keyword retrieval to find pertinent information, then provide it as context alongside the user's prompt. This approach dramatically reduces hallucination, enables access to current information beyond the model's training cutoff, and allows grounding responses in verifiable sources. RAG powers enterprise question answering systems, customer support chatbots, and research assistants that need to reference specific document collections. Implementation typically involves a vector database for efficient similarity search, a chunking strategy for document processing, and careful prompt engineering to instruct the model to use retrieved context faithfully. RAG has become the standard approach for building production AI applications that require factual accuracy.
Category
Generative AI
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Chain of Thought
Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
ChatGPT
ChatGPT is an AI chatbot developed by OpenAI that uses large language models to generate human-like conversational responses. It became one of the fastest-growing consumer applications in history after its launch in November 2022.
Claude
Claude is an AI assistant developed by Anthropic, designed to be helpful, harmless, and honest. It is built using Constitutional AI techniques and competes with models like GPT-4 and Gemini.
Diffusion Model
A diffusion model is a generative AI model that creates data by learning to reverse a gradual noise-adding process. Diffusion models power state-of-the-art image generation systems like Stable Diffusion and DALL-E.
Discriminator
A discriminator is the component of a GAN that learns to distinguish between real and generated data. It provides feedback to the generator, creating an adversarial training dynamic that improves output quality.
Few-Shot Prompting
Few-shot prompting provides a language model with a small number of input-output examples in the prompt to demonstrate the desired task format. This technique helps models understand task requirements without any fine-tuning.
Foundation Model
A foundation model is a large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, and DALL-E are examples of foundation models that serve as bases for specialized applications.
GAN
A GAN (Generative Adversarial Network) is a generative model consisting of two competing neural networks — a generator and a discriminator. GANs produce realistic synthetic data by training these networks in an adversarial game.