Tuesday, 12 November 2024

Prompt Crafting Excellence

 1. Domain Specific Techniques

Emphasize tailoring prompts to specific domains and handling variations within a task.

  • Task-specific vocabulary: Use the specialized terms of the domain (e.g., legal jargon, medical terminology, E-Commerce, etc) for optimal LLM understanding.
  • Conditional prompt: Imagine you're crafting a prompt, but the instructions change based on a specific condition. This condition could be anything from the target audience to the desired outcome of the LLM's response.

Monday, 11 November 2024

Tokens and Prompts in Gen AI

Understanding Tokens and Crafting Effective Prompts for LLMs

Ever wondered how Large Language Models (LLMs) understand your prompts? These models break down prompts into "tokens" - the basic units of language processing, which can be entire words, parts of words, or even punctuation marks. Tokens allow LLMs to analyze and interpret what you’re telling them.

Key Considerations When Using Token-Based LLMs

  1. Token Limits: Each LLM has a maximum number of tokens it can process in a single interaction, known as its "context window." This limit includes both your prompt and the model’s response. Different models have different limits, so it’s essential to check these beforehand. If your input exceeds this limit, the response may be cut off or become nonsensical.

  2. Cost Optimization: Some LLMs charge based on the number of tokens used. Keeping prompts concise can help reduce costs while ensuring that your request is clearly understood.


Prompts: The Roadmap for LLMs

Prompts are the instructions you give the model, guiding its behavior and shaping its response. The quality of a prompt can greatly impact the quality of the output. With well-crafted prompts, you can achieve more accurate, helpful responses; some advanced models can even respond in multiple languages.

Crafting Effective Prompts: Essential Elements

Effective prompts can prevent misunderstandings, reduce resource waste, and enhance the user experience. Here’s how to create high-quality prompts:

  • Clarity and Specificity: Use straightforward language to avoid ambiguity. Define your expected outcome as specifically as possible.
  • Role Definition: State the role or perspective the model should adopt (e.g., "Write as a teacher").
  • Context: Provide background information or details relevant to your request to help the model better understand your needs.
  • Task Instructions: Clearly describe the task (e.g., "summarize this article," "write a poem").
  • Style and Tone: Specify the style (e.g., "formal" or "informal") and tone (e.g., "serious" or "humorous") for the response.
  • Formatting: Mention if you prefer a particular format, like bullet points, essay, or code snippets.
  • Length: Indicate the approximate length of the output if it matters.
  • Examples: Include examples to show the desired outcome, especially for creative tasks.
  • Keywords: Add relevant keywords to guide the model toward specific information or themes.
  • Sources: For fact-based queries, suggest using reliable sources to ensure accurate information.
  • Grammar and Syntax: Use proper grammar and syntax to help the model interpret your request correctly.
  • Refine and Iterate: Don’t hesitate to refine or test different prompt variations for the best results.

By following these best practices, you’ll help the LLM generate clearer, more accurate responses, making interactions smoother and more effective.

Type of Language models in Gen AI

1. Large Language model

The advanced version of LMs, trained on larger datasets and use advanced techniques such as deep learning and transformers to analyze complex relationships between words.


Ex:

  • ChatGPT by Open AI
  • BERT, Palm2, T5 by Google
  • Turing NLG by Microsoft


2. Small Language model

Compact versions of LLMs that require less training data, have simpler architectures, and are quicker to develop. 

Suitable for specific tasks and domains, offering focused expertise, as well as for devices with limited processing power.


Ex:

  • Gemma Models By Google
  • Phi-2 By Microsoft