Overview
This article presents a system for converting Wikipedia articles into comprehensive learning modules using a single Large Language Model with lightweight adapters. The approach addresses educational personalization while tackling significant computational efficiency challenges.
Core Architecture
The system employs one model with control tags embedded in prompts to route between two distinct modes:
- The
<STUDY_GUIDE>tag generates summaries, Q&A, and flashcards - The
<CONCEPT_MAP_TIMELINE>tag produces visual learning aids connecting concepts and historical sequences
The Central Problem
Processing lengthy Wikipedia articles multiple times across different modes creates substantial computational overhead. This is the primary bottleneck to making this system practical, particularly on CPU-limited hardware.
Proposed Solutions
Four optimization strategies are explored:
- Prefix caching engines — Leverage platforms like vLLM that cache shared article text across multiple requests
- Single-pass structured output — Request both artifacts simultaneously, then partition results
- Outline-and-refine — Generate dense notes first, then use condensed versions for each task
- Chunk-and-retrieve — Embed articles into vector indexes to fetch only relevant passages per request
Key Technical Insight
Reusing a cache reliably requires that the same base weights and the same adapters remain active. This constraint explains why simply caching the article text proves insufficient—adapter changes invalidate cached internal model states.
Practical Takeaway
Rather than guaranteeing KV-cache optimization, the focus should be on immediately implementable approaches that deliver comparable user experiences without requiring advanced infrastructure support.