Detecting Policy Puppetry in LLM Prompts: A Simple, Transparent ApproachThere’s a lot of excitement these days about large language models (LLMs) and the new opportunities—and risks—they bring. As someone who’s spent a fair bit of time in the NLP and LLM trenches, I’ve seen firsthand how creative people can get when tryi...Apr 27, 2025 · 3 min readmetawake.hashnode.dev
Flexible Entity Extraction for NLP Pipelines: Introducing nergrepAs an AI/NLP/ML platform engineer, I often need to prototype and iterate on text analytics pipelines. Entity extraction is a frequent, foundational step—whether I'm building quick R&D experiments or shaping components for production. I wanted a tool ...Apr 26, 2025 · 4 min readmetawake.hashnode.dev
Best Practices for Prompt Modifier Shaping: Navigating Model Behavior and ResponsivenessSome models resist the formatting, length, or tone modifiers we give them. But what if that's not a bug, but a feature we can work around? Context and Purpose In recent experiments, we explored how prompt modifiers influence the length, style, and st...Apr 21, 2025 · 3 min readmetawake.hashnode.dev
Prompt Shaping: Measuring the Impact of Prompt Modifiers on Output Size and FormatWhat if you could guide large language models (LLMs) to output shorter, clearer, or more structured responses just by adjusting the prompt? This experiment set out to answer a simple yet valuable question: Can we shrink or reshape LLM output just by...Apr 21, 2025 · 4 min readmetawake.hashnode.dev
Semantic Prompt Compression: Reducing LLM Costs While Preserving MeaningView the open-source project on GitHub The Challenge: Every Token Costs In the world of Large Language Models (LLMs), every token comes with a price tag. For organizations running thousands of prompts daily, these costs add up quickly. But what if w...Apr 14, 2025 · 3 min readmetawake.hashnode.dev
