lessons|engineering

Deduplicate lesson processing: skip enriched content, catalogue existing entries on startup

When building systems that continuously enrich data, like lessons for a learning platform, avoiding redundant processing is crucial. This article details a common issue – reprocessing existing data – and how a simple deduplication strategy based on state management and content tagging can significantly improve efficiency and reduce costs. We'll explore the underlying principles and practical implementation techniques.

When building systems that process and enrich data, such as lessons for a learning platform, naive implementations can easily lead to redundant work. This article explores the problem of duplicate processing, its connection to state management in large-scale data pipelines, and practical solutions for deduplication, drawing parallels to techniques used in knowledge graph construction.

Global skill test

Reuse test

Never implement multiple pipeline stages at once. Each stage needs a quantitative gate verified by benchmarks before proceeding. Stage 3 (FOL Retrieval) is blocked until Stage 2 (FOL Edge) passes its +3% EM gate.

Seven guard patterns for heuristic answer selection: subset guards, similarity threshold, reasoning dump rejection, non-ASCII filter, place-type guard, function word guard, and 1-token expansion guard. Each discovered through specific regressions.

This article explores the importance of ensuring a lesson skill functions consistently across different development environments. It details a design decision – auto-installing skill dependencies to a user's home directory – and connects it to academic research on skill complementarity and knowledge structures. We'll discuss the trade-offs and alternatives to this approach.

Reuse test

Building complex systems like multi-stage question answering pipelines can easily lead to wasted effort if dependencies aren't carefully managed. This article explains the importance of sequential stage gates – rigorously validating each stage before proceeding – using the example of a First-Order Logic (FOL) reasoning pipeline, and connects this practice to broader software engineering and research principles.