LLM Settings

Large Language Models (LLMs) often come with adjustable settings that can significantly affect the output quality and behavior. These settings help you control how the model responds to your prompts. Common LLM settings include temperature, top-p, max tokens, and stop sequences.

• Temperature controls creativity— lower values make responses more focused and predictable, while higher values generate more diverse answers.
• Top-p (nucleus sampling) filters the output to the most probable words, helping maintain coherent responses.
• Max tokens set the length limit for responses.
• Stop sequences tell the model when to stop generating further output.

Understanding and tweaking these settings is important when designing applications or interacting with GenAI tools. They allow for more precision, control, and reliability in generating the kind of text you need.

Basics of Prompting

Prompting is the process of giving clear instructions to an LLM so it understands what kind of output you want. A good prompt guides the model to give accurate, relevant, and useful results. At its core, prompting is about being specific and structured in how you ask.

• For example, instead of saying “Tell me about India,” a better prompt would be, “Write a short paragraph about the culture and festivals of India.” This makes it easier for the model to respond meaningfully.
• Prompting works best when you provide context, intent, and sometimes formatting instructions. You can also experiment by rewording or breaking down questions for more detailed outputs. Mastering the basics of prompting is the first step toward unlocking the full power of GenAI tools.

Prompt Elements

A well-structured prompt is often made up of key elements that guide the model’s output. These include instructions, context, input data, output format, and tone.

• Instructions tell the model what to do (e.g., “Summarize the paragraph”).
• Context helps the model understand the background or domain (e.g., medical, legal, educational).
• Input data is the actual text or information the model will work with.
• Output format defines how the response should look (e.g., bullet points, table, paragraph).
• Tone sets the style – formal, casual, professional, etc.

By combining these elements, you create prompts that are not only clear but also more likely to produce the desired result. This structure becomes especially useful in real-world GenAI applications and automation workflows.