Language AI has evolved from simple word counting to sophisticated models like transformers, aiming to preserve meaning through numerical representation, with future breakthroughs poised to enhance reasoning and contextual understanding.
The LLM T.E.S.T. Framework is a structured approach for evaluating Large Language Models (LLMs) across multiple dimensions. It determines an AI's true capabilities, reliability, and scalability for real-world applications, distinguishing truly useful models from those that merely appear intelligent.
Learn key techniques to optimize small-scale RAG systems for efficient, accurate data retrieval and enhanced performance.
LightRAG leverages graph-based indexing and dual-level retrieval to transform Retrieval-Augmented Generation (RAG), enabling efficient, context-aware information retrieval and seamless real-time data adaptation.
Learn how to build a Retrieval-Augmented Generation (RAG) pipeline for efficient unstructured data processing. This comprehensive guide covers data ingestion, extraction, transformation, loading, querying, and monitoring, addressing key challenges and considerations.
A look at zero-shot prompting, a technique that enables large language models to perform tasks without explicit training data. Explore its benefits, limitations, best practices, and real-world applications.
Query reformulation involves refining and clarifying user queries to enhance the accuracy and relevance of responses from AI systems like ChatGPT or Claude. This technique can improve user interactions, save time in technical domains, and optimize the performance.
Talk to ChatGPT, not at it! Unlock creative content & diverse voices with this guide to human-like interaction. #AIwriting #ChatGPT #FutureOfContent
Ask Me Anything (AMA) Prompting is a novel strategy that aggregates responses from multiple prompts to enhance conversational AI. This simple approach significantly boosts model accuracy without additional training.
One of the most impactful prompting techniques you can use is any method of self-critique. In this lesson, we decouple this from the most familiar promoting strategies and zoom in on this technique.
Getting the most out of large language models requires the artful application of optimization techniques like prompt engineering, retrieval augmentation, and fine-tuning. This guide explores proven methods for maximizing LLM performance.
Large language models like GPT-4 are powerful but opaque "black boxes." New techniques for explainable AI and transparent design can help unlock their benefits while auditing risks.