Long-Form Document Processing Strategies
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About This MicroSim
This interactive decision tree guides students through choosing the right strategy for processing documents that exceed a model's context window. By answering 2-3 questions about their goal (summarize, extract, analyze, or Q&A), document size, and required detail level, students arrive at a recommended strategy with step-by-step instructions and pro tips.
Strategies covered include direct summarization, hierarchical summarization, map-reduce, chunking with overlap, targeted extraction, and more.
How to Use
- Read the question displayed at the top of the decision tree
- Click an answer to proceed to the next decision point or a strategy recommendation
- Review the recommendation including its description, numbered steps, and pro tip
- Click "Start Over" to try a different path and compare strategies
- Click "Back" to revisit a previous decision
Lesson Plan
Grade Level
High School through Adult Learners
Duration
10-15 minutes
Prerequisites
Basic understanding of context windows and token limits in language models.
Activities
- Exploration (5 min): Navigate through all four starting paths (summarize, extract, analyze, Q&A) and note the different strategies recommended.
- Guided Practice (5 min): Given a scenario (e.g., "You have a 200-page legal contract and need to find all liability clauses"), use the decision tree to find the best strategy. Discuss why that strategy fits.
- Discussion (5 min): Compare map-reduce vs. hierarchical summarization. When would you choose one over the other?
Assessment
- Students can identify the appropriate processing strategy for a given document size and task
- Students can explain why chunking strategies differ based on the goal
- Students can describe the tradeoffs between processing speed and detail retention
References
- Wikipedia: MapReduce - The distributed processing paradigm that inspires map-reduce summarization
- LangChain Document Processing - Practical implementations of chunking strategies