First-Principles
AI Education
Structured programs for leadership, architects, and engineers — built on foundational understanding, not tool dependency.
The AI Competency Crisis
Most organizations have adopted AI tools. Very few understand what those tools actually do, why they work, or when they fail. This gap between adoption and comprehension represents the single largest unmanaged risk in enterprise technology today.
Tool-based training teaches people to operate interfaces. First-principles education teaches people to reason about systems. The difference determines whether your organization can evaluate AI capabilities independently, or whether it remains permanently dependent on vendor narratives.
Tool-Trained, Not Fluent
Of enterprise teams that have completed AI training programs, the majority can operate a specific tool but cannot evaluate alternative approaches or diagnose failures independently.
Higher Vendor Lock-in
Organizations with tool-dependent training are four times more likely to accept vendor cost increases without evaluating alternatives.
Average Misallocation
Enterprise AI budgets misallocated annually due to technical leadership that cannot independently evaluate model capabilities or total cost of ownership.
Tool Obsolescence Cycle
Average lifespan of an AI platform interface before significant change. First-principles knowledge remains relevant for decades.
First Principles, Not Tutorials
Our programs are built on a single conviction: understanding precedes capability. Every module is designed to build structural comprehension — the kind of knowledge that survives platform changes, model generations, and industry shifts.
We do not teach tools. We teach the reasoning that makes all tools intelligible. When a participant completes our program, they understand why a system works, where it will fail, and what alternatives exist.
Mathematical Foundations
Linear algebra, probability, optimization — not as academic exercises, but as the operational substrate of every AI system.
Architectural Reasoning
How transformer architectures work. Why attention mechanisms scale. What retrieval-augmented generation actually does to inference quality.
Failure Mode Analysis
Hallucination, mode collapse, distribution shift, prompt injection. Every AI failure mode is a consequence of architectural properties.
Vendor Independence
When your teams understand fundamentals, they can evaluate any platform, any model, any vendor claim against first principles.
Each layer is independently valuable. Any participant can enter at the layer appropriate to their role without prerequisites from lower layers. But every layer is built on the same foundational framework — ensuring consistent vocabulary, mental models, and decision-making criteria across the organization.
What This Program Is Not
Clarity requires boundaries. The most important thing we can tell you about this program is what it deliberately excludes — and why those exclusions make it more valuable, not less.
This Is Not
This Is
The test is simple: If a tool changes its interface tomorrow, does your team's knowledge still hold? If a new model architecture emerges next quarter, can your architects evaluate it independently? First-principles education answers both with confidence.
Strategic AI Comprehension
Leadership does not need to write code. But leadership does need to understand AI at a systems level — well enough to evaluate vendor claims, approve architecture decisions, assess risk, and govern AI deployments with genuine technical confidence.
AI as Systems Engineering
What AI systems actually are: statistical models, not intelligence. How inference works. What training means. Why "accuracy" is misleading without context on precision, recall, and distribution.
Risk Architecture
Hallucination as a structural property. Data sovereignty as a compliance requirement. Model drift and its operational consequences.
Economic Models of AI
Token economics versus infrastructure ownership. TCO analysis for public API vs. private deployment. Budget governance for AI programs.
Governance & Accountability
Model versioning and audit trails. Explainability requirements by regulatory context. Board-level AI reporting frameworks.
Vendor Evaluation Frameworks
Independent evaluation criteria for model performance, data handling practices, lock-in risk, and total cost of ownership.
Participants leave with the vocabulary, mental models, and evaluation frameworks to make AI decisions at the strategic level — without dependence on vendors or technical intermediaries.
AI System Architecture & Decision Frameworks
The architect's challenge is not understanding AI in isolation — it is understanding how AI integrates into production systems with existing security, data, and operational constraints.
Transformer Architecture Deep Dive
Attention mechanisms, positional encoding, multi-head attention — explained as system design patterns.
Model Selection & Sizing
8B vs. 70B vs. 180B parameter models: capability boundaries, inference costs, hardware requirements. Open-weight vs. proprietary trade-offs.
RAG & Retrieval Systems
Vector databases, embedding models, chunking strategies, relevance scoring. When RAG improves quality and when it introduces latency without value.
Inference Infrastructure
GPU compute planning, quantization strategies (GGUF, GPTQ, AWQ), inference servers (vLLM, TGI), throughput optimization.
Agentic Systems Design
Multi-step AI workflows: tool use, function calling, chain-of-thought orchestration. Safety boundaries for autonomous AI actions.
Security & Data Architecture
Prompt injection, data poisoning, model extraction. Designing data pipelines that maintain sovereignty through the AI inference lifecycle.
Participants gain system-level reasoning to design AI architectures that meet production requirements — selecting models, planning infrastructure, and maintaining security without vendor guidance.
Technical Foundations & Implementation
This is where mathematics meets implementation. Layer 3 does not assume prior ML experience, but it does assume engineering aptitude. Participants build understanding from the mathematical substrate upward.
Mathematical Foundations
Linear algebra for embeddings. Probability theory for generative models. Calculus for optimization. Information theory for loss functions.
Neural Network Architecture
Forward propagation, backpropagation, gradient descent, learning rate scheduling. Why networks converge and when they don't.
Language Model Internals
Tokenization, embedding spaces, attention computation, next-token prediction. Temperature, top-k, and top-p at the distribution level.
Fine-Tuning & Adaptation
Full fine-tuning vs. LoRA vs. QLoRA. RLHF and DPO alignment techniques. When fine-tuning improves task performance vs. prompt engineering.
Evaluation & Benchmarking
BLEU, ROUGE, perplexity — and why they often mislead. Designing task-specific evaluation frameworks. Human evaluation protocols.
Production Engineering
Model serving, API design, caching strategies, monitoring. MLOps principles without vendor lock-in. Version control for models and data.
First-principles comprehension of neural network architecture, language model mechanics, fine-tuning methodology, and production deployment — validated through practical assessment.
Curriculum Architecture
Twelve modules, three layers. Each module is self-contained but designed to compound with others. Organizations can deploy the full program or select modules by role and priority.
| # | Module | Layer | Duration |
|---|---|---|---|
| 01 | AI as Systems Engineering — What AI Is and Isn't | L1 | Half-day |
| 02 | Risk, Governance & Regulatory Landscape | L1 | Half-day |
| 03 | AI Economics — TCO, Token Costs & Build vs. Buy | L1 | Half-day |
| 04 | Transformer Architecture & Attention Mechanisms | L2 | Full day |
| 05 | Model Selection, Sizing & Open-Weight Landscape | L2 | Full day |
| 06 | RAG, Retrieval Systems & Vector Architecture | L2 | Full day |
| 07 | Agentic Systems & Multi-Step Orchestration | L2 | Full day |
| 08 | Mathematical Foundations for AI | L3 | 2 days |
| 09 | Neural Networks — From First Principles | L3 | 2 days |
| 10 | Language Model Internals & Generation | L3 | 2 days |
| 11 | Fine-Tuning, Alignment & Adaptation | L3 | 2 days |
| 12 | Production Engineering & MLOps | L3 | 2 days |
Delivery Formats
Three formats, calibrated by audience and depth. Each is independently deployable. Organizations typically begin with Leadership Briefings, then extend to Technical Workshops and Certification Programs.
Leadership Briefings
Layer 1 · StrategicHalf-day executive sessions. Dense, structured, technically grounded. Calibrated for time-constrained senior leaders.
Audience: CIOs, CTOs, VPs of Engineering, Board-level technology advisors, AI program sponsors.
Outcome: Technical vocabulary, systems-level mental models, risk evaluation frameworks, vendor assessment criteria.
3 × half-day sessions | 8–15 participants | In-person or virtual
Technical Workshops
Layer 2 · ArchitectureFull-day intensive workshops. Hands-on architecture exercises, model evaluation labs, system design reviews.
Audience: Solution architects, engineering managers, senior developers, DevOps/MLOps leads, technical product managers.
Outcome: Ability to design AI system architectures, evaluate model trade-offs, and make build-vs-buy decisions grounded in technical analysis.
4 × full-day sessions | 10–20 participants | In-person preferred
Certification Programs
Layer 3 · TechnicalMulti-day deep programs with assessment. Mathematical foundations, implementation exercises, capstone projects.
Audience: Software engineers, ML engineers, data scientists, CS graduates, technical professionals transitioning to AI.
Outcome: First-principles comprehension validated through practical assessment, not multiple-choice exams.
10 days (modular) | 12–24 participants | In-person + lab environment
Available Programs
Browse and enroll in currently available programs. Filter by category or level to find the right fit for your role and experience.
Understanding Precedes Capability.
AI fluency is not a training exercise. It is the foundation on which every informed decision, every sound architecture, and every responsible deployment is built.
education@alcomtechnologies.com
enterprise@alcomtechnologies.com