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About

Version: 2.3.0
Birth: 2025-10-19
Last Modified: 2026-01-31

This repository provides a systematized, AI-engineered knowledge base for building production-grade AI systems. It is the practical manifestation of a hybrid methodology that orchestrates capable Large Language Models (LLM) alongside local Small Language Models (SLM).

All content is generated, validated, and versioned through a controlled Language Model (LM) workflow—treating documentation as executable, testable artifacts rather than static prose.

Mission: To enable MLOps engineers and AI architects to construct reliable, audit-ready AI infrastructure. We prioritize a hybrid approach: using high-reasoning cloud LLMs for architecture and planning, while utilizing local SLMs for secure, resource-efficient coding and execution.

What’s new?

v2.3.0

v2.2.0

v2.1.0

Authorship & Methodology

My name is Vadim Rudakov, I am a Systems Engineer and AI Methodology Architect from Sverdlovsk oblast, Russia. This repository is a “practitioner-focused” engineering hub designed to solve a specific problem: the fragility of AI prototypes and the risk of institutional “tribal knowledge”.

The repository is licensed under GPLv3 for its core assets and CC-BY-SA 4.0 for the article content.

Coverage

Content is organized around the six layers of LLM systems:

  1. Execution & Optimization: CPU/GPU hybrid pipelines, memory (VRAM/RAM) management, NVIDIA tuning

  2. Model Development: SLM selection, tokenization, embedding, security

  3. Prompt Engineering: Modular design, XML schemas, template lifecycle

  4. Orchestration: RAG, workflow chaining, structured output control

  5. Context Management: Vector stores, hybrid retrieval, indexing strategies

  6. Security & Quality: Bias detection, access control, validation templates (spread across all other layers)

All materials are LM-generated, then cross-validated by multiple models, and deeply reviewed before promotion from draft to final by me. The production of one handbook may take tens of manual and AI-backed iterations before I am ready to put it into the main branch, but I understand it can still have problems, so feel free to leave issues.

Generation Workflow: Hybrid Orchestration

All content follows a reproducible pipeline mirroring software CI/CD, specifically leveraging hybrid LLM/SLM capabilities:

  1. Capable LLM step:

    1. Idea elaboration: local_ai_systems_consultant.json helps to peer-review the ideas and solutions in the chat form to stress-test concepts before they’re adopted as a methodological tool.

    2. Draft generation: handbook_consultant.json produces articles and diagrams.

    3. Cross-validation: Multiple models critique and refine outputs.

  2. Human review step:

    • Final sanity check before promotion to published directories

  3. CI/CD:

    • Version control: Every file is tracked, diffed, and tested like source code with the help of the pre-coomit hooks, GH actions, and local SLM.

In this paradigm: prompts = source code, articles = build artifacts, reviews = QA gates.

Repository Structure

The core part of the repository is the ai_system directory which introduces 5 layers of AI system. Read more about this approach in “A Multi-Layered AI System Architecture”.

.
├── 0_intro/                # Introductory material, high‑level overviews, background resources
├── ai_system/              # Core implementation of the six LLM‑system layers
│   ├── 1_execution/        # Latency control (<100ms), CUDA optimization, etc.
│   ├── 2_model/            # Model selection, adaptation, fine-tuning, etc.
│   ├── 3_prompts/          # Prompt-as-Infrastructure: formatting and transportation, GitOps versioning, etc.
│   ├── 4_orchestration/    # Wrokflow, agent specialization, etc.
│   ├── 5_context/          # RAG, knowledge bases, etc.
│   └── security/           # Centralized policy hub: adversarial threat modeling, RBAC, and OWASP LLM risk mitigationtools
├── architecture/           # Architectural decision records & post‑mortems
│   ├── adr/                # Formal ADR documents describing major design choices
│   └── post-mortems/       # Retrospectives on failures and lessons learned
├── misc/                   # Miscellaneous, non‑core work
│   ├── in_progress/        # Drafts, experiments, work‑in‑progress artifacts
│   └── pr/                 # Pull‑request notes, review checklists, temporary files
├── mlops/                  # MLOps‑focused tooling and pipelines
│   ├── ci_cd/              # CI/CD configs, GitHub Actions, quality‑gate scripts (broken‑link checks, Jupytext verification)
│   └── security/           # MLOps‑specific security tooling, secret‑scanning, compliance checks
├── research/               # Experimental research code & notebooks
│   └── slm_from_scratch/   # Tokenizers, model‑training loops, low‑level SLM experiments
└── tools/                  # Supporting utilities for the repo
    ├── docs/               # Documentation generation helpers, style guides, reference material
    ├── prompt_helpers/     # Prompt libraries, system‑prompt JSON files, validation scripts
    ├── scripts/            # General‑purpose automation (link checking, formatting, Jupytext sync, etc.)
    └── tests/              # Test suites for tools & research code

Research & Foundations (/research)

While the core layers of this repository focus on the systemic orchestration of AI, the research/ directory is dedicated to the first principles of Small Language Models. We treat the transition from a single neuron to a production-grade Transformer not as a “black box,” but as a verifiable engineering stack.

In this repository, “Research” is not academic—it is forensic. By building the model from scratch:

  1. Security is Hardened: We understand exactly how data flows through the weights, making it easier to identify points of failure for prompt injection or data leakage.

  2. Optimization is Precise: Hardware-aware tuning (Layer 1) is more effective when you understand the computational graph of the model (Research Layer).

  3. Validation is Absolute: We use manual gradient checks in research to ensure that our automated validation protocols in the MLOps layer are mathematically sound.

Motivation: Sustainable AI Engineering

My goal is to build AI solutions maintainable as ordinary software. Every problem documented here was confronted while developing local SLM stacks under resource constraints. I share this repository to:

Contact Information

Vadim Rudakov, lefthand67@gmail.com

AI Engineering Book
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