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Sunil Ramlochan
Bridging AI theory with Practice and Implementation
523 postsPosts by Sunil Ramlochan
Building a Robust Prompt Library for Effective Task Execution
GuideDevelop a prompt library with broad templates refined into specific use case recipes. Ensure precision and utility through iterative refinement and specialized templates, with proper indexing for organization and traceability, demonstrated in medical report creation.
Building a Robust Prompt Library for Effective Task Execution
Objective: To develop a comprehensive library of broad prompt templates that spans across a chosen domain, allowing for iterative refinement and specialisation to create specific use case prompt recipes. This library will evolve over time, enhancing its utility and precision.
Key Concepts
- Broad Prompt Templates
- Iterative Refinement to Create Use Case Prompt Recipes
- Evolution of Specialised Templates
- Nomenclature and Indexing for Traceability
Detailed Framework
1. Building a Library of Broad Prompt Templates
Definition: Broad prompt templates are high-level, versatile frameworks designed to cover a wide range of tasks within a chosen
Getting Started with Few-Shot Prompting
GuideDiscover the power of F-Shot Prompting in enhancing the performance of large language models. Learn how providing examples improves accuracy and reliability. Explore practical applications and advanced techniques to optimize your AI interactions.
Query/Prompt Reformulation is Magic
Prompt EngineeringQuery reformulation involves refining and clarifying user queries to enhance the accuracy and relevance of responses from AI systems like ChatGPT or Claude. This technique can improve user interactions, save time in technical domains, and optimize the performance.
There was an interesting study done earlier this year called What’s the Magic Word? A CONTROL THEORY OF LLM PROMPTING.
The study applied control theory to prompt engineering in Large Language Models (LLMs), demonstrating that short prompts can significantly influence the output, thus providing a foundational understanding of LLM controllability or what they termed "Magic Words".
Summary and Overview of the Study
This research attempts to address how to mathematically formalize prompt engineering for large language models (LLMs) through the lens of control theory (a fundamentally flawed endeavour but that's for another time).
From a practical aspect, the study
Miniscripts & Processors
Prompt LibraryPrompt Library - These are a collection of miniscripts and processors, designed for efficiency and productivity. You won't find a library like this anywhere else.
Agents
Prompt LibraryPrompt Library - These are a collection of AI Agents, designed for novelty, nuance and originality. You won't find a library like this anywhere else