llm foundations

The problem

Developers often struggle with LLM implementations because they rely on fragmented definitions rather than a cohesive mental model. Without understanding the causal dependencies between tokenization, attention, and inference, debugging RAG pipelines or fine-tuning models becomes guesswork.

What it does

• Constructs dependency-aware learning paths that explain how LLM components like embeddings and transformers connect.
• Explains the internal data flow of mechanisms rather than just providing static definitions.
• Distinguishes between pretraining, fine-tuning, prompting, and RAG based on what actually changes inside the system.
• Identifies specific failure modes in architectures and retrieval systems to improve root-cause analysis.
• Validates understanding through mechanism tracing and 'what-if' scenarios to ensure conceptual mastery.

Why this beats prompting it yourself

Standard prompts often produce surface-level analogies or hallucinated technical specs. This skill enforces a rigorous protocol that separates established facts from inferences, respects architectural dependencies, and prevents the learner from advancing until the foundational mental model is verified.

Use cases

• Onboarding engineers to LLM projects by building a first-principles understanding of transformer architectures.
• Diagnosing why a RAG system is failing by tracing dependencies from retrieval quality to context window limits.
• Evaluating the trade-offs between LoRA, QLoRA, and full fine-tuning for specific hardware constraints.
• Mastering the nuances of tokenization and decoding strategies to optimize model latency and output quality.