Langflow is an open-source visual programming framework built on top of LangChain. It offers a graphical interface for building and managing applications that use large language models (LLMs). With Langflow, developers (and even non-developers) can design, test, and deploy LLM-powered workflows using a no-code/low-code approach.
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LangChain is an open-source framework designed to help developers build applications that leverage language models (LLMs). It provides tools and abstractions to simplify the development of applications centered around natural language processing (NLP), such as language generation, retrieval, summarization, and question answering.
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Prompt Engineering or Prompting is the process of structuring or crafting an instruction or prompt in order to produce the best possible output from a generative artificial intelligence (AI) model. A prompt is natural language text describing the task that an AI should perform. A prompt for a text-to-text language model can be a query, a command, or a longer statement including context, instructions, and conversation history. (Wikipedia).
Read MoreA 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.
Read MoreLLMs are typically developed through a process of training on vast amounts of data, the corpus. This costs a lot of time and money. ChatGPT-3, for example, cost $10M. This cost going down but it’s remains expensive. You can avoid this cost for specific use cases by “fine-tuning” a model with specific data or you can augment their prompts with reference data as in Retrieval Augmented Generation or RAG. The next stage in LLM development are models that update/evolve through time. This is what’s discussed in Sakana AI’s paper Transformer²: Self-Adaptive LLMs.