Large Language Models (LLM) : A computer program designed to understand and generate human-like text. They are trained on massive amounts of text data, which allows them to learn the patterns and rules of human language. These models are designed to process and comprehend natural language, making them capable of tasks such as text generation, language translation, summarization, and answering questions. It is the Brain of Language Processing
Temperature : Temperature is a parameter that controls the randomness of the model’s output. Think of temperature as a control knob that adjusts how creative an LLM’s output is. A higher temperature means the LLM is more likely to come up with unexpected and original ideas, while a lower temperature keeps the output more predictable and factual.
Vector DB : Now to store all this information, vector database is used. Each piece of info has its own spot, making it easier for the model to find and use when you ask a question. It is a specialized type of database that stores and manages vector embeddings. It offer several advantages for GenAI applications: Efficient Search and Retrieval, Integration with AI Frameworks, Scalability
Embeddings : Think of embeddings as numerical labels assigned to words and phrases. These labels capture the semantic relationships between words, empowering LLMs to understand the meaning of language and generate meaningful text.So, embeddings are like secret codes for words, and the vector database is the well-organized library where GenAI keeps these codes. Together, they make sure GenAI understands what you’re saying and can respond in a way that makes sense.
Chunk : They are the building blocks that LLMs use to construct sentences and paragraphs. The choice of chunk size depends on the specific task at hand, influencing the structure and flow of the generated text. Chunks can be individual words, phrases, or even larger segments of text. It is the units that GenAI models use to construct sentences and paragraphs.
Prompt: The Instruction Manual for Language Generation. Think of a prompt as a set of guidelines that directs an LLM’s output. It’s like providing a chef with a recipe to follow. For instance, a prompt could instruct an LLM to write a resume, translate a sentence from English to Spanish, or answer a question about a scientific topic.
In essence, LLMs, temperature, embeddings, chunks, and vector databases work together like a sophisticated communication system, allowing LLMs to understand and generate human language effectively.
Putting it all together
LLM is the large language model , which is like a language wizard that understands words and sentences. To talk to this model, you give it a special question or statement called a prompt. This prompt helps LLM know what your question is , or what you want to chat about. LLM has this cool library called a vector database. In this library, every word has its own special code (embedding), like a secret language fingerprint. These embeddings help LLM understand the meaning of words. Now, here’s where things get interesting. LLM also uses “chunks,” which are like small, meaningful puzzle pieces of words. It breaks down your sentences into these chunks to understand them better. Temperature lets you control how creative LLM gets in its responses. Turn it up for more fun and randomness or down for focused and serious answers.
So, when you chat with LLM using a prompt,it checks the vector database for word codes (embeddings), plays with temperature for the right vibe, and uses chunks to make sense of what you’re saying.
