Building a Tiny Language Model (LLM) in Ruby: A Step-by-Step Guide - V3 "Integrating Reasoning into the Tiny LLM"

A new method added to the Markov Chain language model simulates "chain-of-thought" reasoning, generating intermediate tokens before the final output to improve coherence and context. This two-phase process involves reasoning generation and final output generation. While simpler than neural LLMs, it demonstrates how additional processing layers can be integrated into language models, offering opportunities for further experimentation.