-
Rules vs model bake-off: measuring when learned models actually earn their complexity
A machine learning model achieves 77.5% accuracy. A handful of hand-written rules achieves 76.0%. The model wins the benchmark.
-
Guarding LLMs with rules: validators, policy engines, and deterministic output contracts
Large language models are excellent at generating structured responses. Ask one to return JSON, and it can produce a convincing object containing customer-support actions, database queries, API parame
-
Expert systems to neuro-symbolic: what survived from GOFAI and why it’s coming back
The rule-based systems of the 1970s could explain their decisions, follow explicit constraints, and construct chains of logical deductions. They could also fail spectacularly when reality didn’t fit t
-
This is not an ML problem: constraint solvers with OR-Tools CP-SAT
Imagine you’re building a scheduling system for a fulfillment center.
-
Fuzzy logic and scoring systems: from crisp thresholds to weighted scorecards
Many software systems begin with simple questions:
-
Rule-based NLP that still wins: regex, spaCy Matcher, and hybrid rule+ML pipelines
Large language models have changed what is possible in natural-language processing, but they have not changed a more basic engineering fact: when a text pattern is narrow, stable, and precisely specif
-
Business rules engines: Drools, GoRules, and when domain logic must not be learned
Machine learning is excellent when the question is predictive:
-
RuleFit and skope-rules: mining crisp rules out of a trained ensemble
Tree ensembles are excellent at discovering nonlinear thresholds and feature interactions. A boosted classifier can learn that a large order is suspicious only when the account is young, or that a dis
-
Rule learning: RIPPER, decision lists, and models that output human-readable rules
Most classifiers return a label or probability. A rule learner returns logic that a person can inspect and execute:
-
Ship the rule-based baseline first: why 20 lines of if/else gate every good ML project
When people start an ML project, the instinct is often to train a model immediately. Open a notebook, split the data, fit a classifier, and compare metrics.