Prompt engineering is crafting the instruction text for an LLM (instructions, examples, formatting) to steer its output. Context engineering is the system-level work that programmatically assembles everything the model sees at inference—prompts, re...

This video explains two key approaches to optimizing large language models (LLMs): context optimization (using prompt engineering and retrieval augmented generation or RAG) and model optimization through fine tuning. Using a retail store analogy, the...

(00:00) AI search is transforming how we locate and consume information online, but how? Well, back in the day, search engines were pretty simple because they were based more or less just on keyword search. They matched words in a user's query to ...

In this video, IBM Technology explains several techniques to improve AI accuracy. The speakers discuss methods such as Retrieval Augmented Generation (RAG), choosing the proper model, Chain of Thought prompting, LLM chaining, Mixture of Experts (MoE...

This video explains three approaches to improving large language model outputs: Retrieval Augmented Generation (RAG), Fine-Tuning, and Prompt Engineering. It covers how each method works, the benefits they offer, and the trade-offs involved in applyi...

This video compares LangChain and LangGraph—two open source frameworks for building applications with large language models. It explains each framework’s architecture, components, and state management approaches, and outlines the scenarios where ...

This video explains two methods—Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG)—to overcome the knowledge limitations of large language models. It details how each technique processes and utilizes external information, c...

**(00:02)**   Let's get started! I'm really excited for this session on LLMs, AI search, and strategies for optimizing in this new search paradigm. I'm Ethan, CEO of Graphite. We’re an SEO agency and build SEO tools, developing strategies for co...

The article from Search Engine Land discusses the impact of Search Generative Experience (SGE) and retrieval-augmented generation (RAG) on the future of search engines and SEO. Here are the key points: 1. **Search Generative Experience (SGE):** SGE...