Reprompt Walkthrough & Demo

In this video, I do a walkthrough of Reprompt, a lightweight Python library I created to enhance prompt clarity-without relying on large language models (LLMs).
Reprompt is built using natural language processing techniques to help users refine their prompts without the extra cost or latency of making additional calls to an LLM.
In this video, I’ll walk you through:
How Reprompt analyzes input prompts for key concepts and intent
How it restructures prompts using regular NLP techniques
A fun demo using a prompt about how to cook jollof! 🍲
Key Features 🔧
Analyzes input prompts to pull out key concepts and intent.
Produces structured XML-based prompts for clarity.
Tailors instructions based on the detected intent.
Leverages best practices from Zack Witten’s 2024 prompt engineering workshop.
How It Works 💡
1. Preprocessing: Fixes basic grammar and capitalization.
2. Key Concept Identification: Extracts important keywords/phrases.
3. Intent Recognition: Identifies whether the prompt is asking for an instruction, explanation, etc., by looking for intent-driven words.
4. Tailored Instructions: Adjusts instructions based on recognized intent.
5. Structured Output: Outputs an XML-structured prompt with sections for context, instructions, and output format.
Limitations ⚠
Doesn’t use machine learning for deep prompt analysis.
No few-shot examples (yet!).
Will struggle with complex or multi-layered prompts.
Intent recognition is rule-based, so it might miss subtle nuances.
Check out the code on GitHub:
github.com/kwe...
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