Welcome to The Reasoning Codex¶
A collection of expert playbooks on building reliable, specialized, and grounded AI systems.
This portfolio is dedicated to the science and engineering of applied AI. The playbooks and articles here explore a central thesis: that the path to robust, high-performance AI lies not in the brute force of massive, generalist models, but in the precision and efficiency of smaller, open-source models that are expertly fine-tuned to the data and the task at hand.
The content focuses on a rigorous, three-phase methodology for model specialization:
- Domain Adaptation (e.g., DAPT/CPT)
- Task Specialization (e.g., SFT)
- Behavioral Alignment (e.g., RLFT)
This is a resource for practitioners dedicated to building AI that is not only powerful, but also auditable, efficient, and deeply aligned with real-world, domain-specific challenges.
Available Playbooks¶
A graduate-level playbook on building and fine-tuning state-of-the-art retrieval systems. This guide covers the journey from foundational theory and modern architectures (BM25, SPLADE, ColBERT) to the advanced DAPT → SFT workflow for creating high-performance domain experts.
An advanced playbook on moving beyond prompt engineering to train autonomous AI agents. This guide details the CPT → SFT → RLFT stack, using methods like GRPO to teach smaller language models how to behave: when to retrieve, which tools to use, and when to stop or abstain.