Member-only story
How I Built an Adaptive Concept Explainer Using Hugging Face Models
Demystifying Complex Ideas Through Multi-Level Explanations
Have you ever found yourself trying to wrap your head around a complex concept, or having to break down something technical for someone who doesn’t have the same background? I created a free tool to solve this problem. It provides progressively detailed explanations of any concept — from simple, five-year-old friendly descriptions to expert-level technical breakdowns.
Introduction
In our information-rich world, understanding complex topics can be challenging, especially when explanations are pitched at the wrong level of expertise. Whether you’re a student struggling with quantum physics, a professional trying to grasp machine learning, or simply curious about game theory, the gap between beginner-friendly resources and expert-level content can be frustrating.
The Power of Hugging Face Models
I built the concept explainer tool by using a pair of powerful language models from Hugging Face that run the explanation pipeline:
1. Mistral-7B-Instruct-v0.2: This is a cutting-edge 7B scale model that is very effective for complex instruction-following problems and which produces explanatory prose rich in detail. Its ability to maintain coherence throughout lengthy explanations makes it perfect when handling advanced and expert-level content.
2. Falcon-7B-Instruct: High-quality instruction-tuned model that gives very good results for slightly different kinds of explanations. I included it as an alternative so users have options on how to convey concepts.
These models excel at concept explanation because they can adjust their output when given carefully engineered prompts. By defining a set of differently structured prompts for each difficulty level, I can produce explanations which truly suit the target experienced level — from simple children’s metaphors to scholarly technological jargon.
How does the tool work?
However, the primary challenge to make the concept explainer really work was to set the optimal prompt for each explanation layer. Here’s…
