Understand the fundamental concept of LLMs, their architecture, and their capabilities.

Explore the historical development of language models, highlighting key milestones that shaped modern Large Language Models (LLMs).

Understand the diverse applications of LLMs across industries and their transformative impact on businesses and society.

Prompt engineering is the process where you guide generative artificial intelligence (generative AI) solutions to generate desired outputs.

Understanding the architecture of these models is key to understanding how they work and generate their outputs.

For language models, the smallest unit they use to process text is called a token.

To unlock the full potential of LLMs, we need to understand the parameters that allow us to customize and fine-tune the output.

In the context of language models, temperature is a parameter that controls the degree of randomness in text generation.

Top-p is a parameter that shapes the diversity and quality of the text produced by a generative AI model.

The token limit refers to the maximum number of tokens an AI model can process at one time.

Top-K Sampling is a technique used during text generation to filter out less probable next-word predictions.

This quiz tests your knowledge of LLM architecture and key hyperparameters like temperature, Top-p, Top-k, max tokens, and tokens definition.

Prompt engineering is an iterative process, meaning it requires repeated cycles of designing, testing, refining, and optimizing prompts.

Understanding how to structure prompts effectively is key to making the most out of these advanced models.

This course equips you with advanced strategies and best practices for crafting precise, effective prompts to maximize the accuracy, relevance, and clarity of responses from generative AI models.

This quiz assesses your understanding of the key strategies and best practices for crafting effective prompts to maximize the quality and relevance of responses from generative AI models.

This course focuses on advanced strategies in prompt engineering to optimize communication with Generative AI models, breaking complex tasks into manageable subtasks for improved accuracy and efficiency.

Fine-tuning is the process of adapting a pre-trained model to work better with your specific data, making it more effective for specialized tasks.

Retrieval-Augmented Generation (RAG) is an advanced technique that enhances the capabilities of a large language model (LLM) by combining it with external knowledge sources.

Explore the primary types of biases that often arise in LLMs: gender bias, cultural bias, political bias, and stereotypical bias.

Responsible AI involves addressing key ethical challenges, including transparency, bias, fairness, and privacy, to ensure that these technologies benefit society as a whole.

Explore the most powerful and widely used LLMs in the market, their unique features, applications, and how they compare with one another.

As a developer or user working with AI tools, it's essential to understand the best practices for creating effective prompts, so you can guide AI models toward generating desired outputs efficiently.