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Large Language Models – LLM

What are Large Language Models (LLMs)?

Large language models (LLMs) are essentially computer programs adept at understanding and generating human language. They’re a type of artificial intelligence (AI) trained on massive amounts of text data – that’s what the “large” in their name refers to. This data can include books, articles, code, and even conversations, allowing LLMs to grasp the nuances of language and churn out human-like text in response to a wide range of prompts and questions.

How LLMs work:

  • Deep Learning: LLMs are powered by a type of deep learning called transformers, which are essentially complex algorithms that can analyze sequences of data, like words in a sentence. By processing massive amounts of text, these transformers learn the patterns and relationships between words, enabling them to understand context and meaning.

  • Training: During training, LLMs are given a humongous amount of text data and asked to predict the next word in a sequence. As they go through this process, they refine their ability to understand language and generate coherent responses.


LLMs have a wide range of applications, including:

  • Text generation: LLMs can be used to create different creative text formats, like poems, code, scripts, musical pieces, and even emails.
  • Machine translation: LLMs can translate languages more accurately by considering the context of the text.
  • Chatbots: LLMs can power chatbots that can hold conversations with humans in a more natural way.
  • Summarization: LLMs can generate summaries of factual topics.

However, it’s important to remember that LLMs are still under development, and they can sometimes generate inaccurate or misleading information. It’s crucial to be aware of their limitations and to use them responsibly.

In the series LLM

  • What are Large Language Models (LLMs)?
  • Understand RNNs, their application, and limitations
  • What are Transformers?
  • Understand Attention Mechanism and Transformer Architecture
  • What are tokenizers?
  • What are embeddings?
  • Generating text and summarizing dialogues using LLMs
  • What is Prompt Engineering?
  • Learn optimization techniques like Prompt Engineering, Fine-Tuning, and PEFT
  • Fine-tune LLMs for improved performance on tasks
  • Evaluate model performance using the ROUGE metric
  • Understand RLHF for improved model output.
  • What is knowledge grounding?
  • Implement Retrieval Augmented Generation (RAG) for knowledge grounding
  • Building a chatbot application for an e-commerce use case