LangChain is an open-source framework designed to help developers build applications that leverage language models (LLMs) like GPTs and other AI-based models. It provides tools and abstractions to simplify the process of building applications that involve language generation, retrieval, summarization, question answering, and other tasks where the core is natural language processing (NLP).
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AI image generators have become extremely popular since Midjourney burst onto the scene. Since then, many other AI generators have emerged, but Midjourney remains somewhat inaccessible, and others come with strict limitations. However, I heard that Grok offers image generation with generous limits, so I decided to give it a try.
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Retrieval Augmented Generation or RAG is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information (Wikipedia) . It is another fundamental LLM customization technique that is relatively simple, low-cost, and still requires no model modifications.
Read MorePrompt Engineering or Prompting is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model (Wikipedia). It is the most fundamental LLM customization technique and is simple, low-cost, and requires no model modifications.
In this post, we will explore some common prompting techniques such as:
A large language model or LLM is a type of machine learning model designed for natural language processing or NLP. These models have an extremely high number of parameters (trillions as of this writing) and are trained on vast amounts of human-generated and human-consumed data. Due to their extensive training on this data, LLMs develop predictive capabilities in syntax, semantics, and knowledge within human language. This enables them to generate coherent and contextually relevant responses, giving the impression of intelligence.
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