Large Language Models (LLMs) are advanced tools developed by companies like OpenAI, Anthropic Claude, and others, trained on vast public datasets to understand and generate human-like text. They predict the next word or token in a sentence to generate text. However, challenges like continuous refinement of prompts, limitations in training data, lack of real-time information access, and cognitive limitations exist.

To address these challenges, a concept called Retrieval Augmented Generation (RAG) is introduced. RAG involves breaking down text into smaller chunks, processing them through a specialized model to generate embeddings, and storing these embeddings in a database. When a query is received, the system retrieves relevant records based on similarity to the query embedding and provides them as context to the LLM for generating precise responses.

Agents, a new evolution in LLM utilization, enable LLMs to exhibit a form of cognition, allowing them to react to various inputs and perform actions beyond content creation. Agents can search the web, run code, provide real-time updates, and respond to human feedback. The article suggests following a subsequent article for insights into multi-agent platforms, their construction, functioning, and code samples.

While LLMs are revolutionary, they are still in early stages and require active involvement for optimal performance. Despite their limitations, LLMs and agents represent significant advancements in AI technology.

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