Knowledge base
AI Skills & How They Work
What AI systems can actually do, how they learn, and how to use them without fooling yourself or your users.
10 topics · 10 articles
- What AI Skills Actually Are (and Are Not)
AI skills are not magic and not a product: they are specific capabilities, language understanding, pattern recognition, and genera…
- Prompt Engineering as a Science, Not a Trick
Prompt engineering is the practice of shaping model behavior through instructions, examples, and structure, and it is closer to ex…
- How Neural Networks Learn
Neural networks learn by adjusting billions of weights to reduce a loss function across training examples, using gradient descent …
- Fine-Tuning vs RAG: When to Use Which
Fine-tuning changes model weights to teach new behavior or style; RAG keeps weights frozen and injects relevant documents at query…
- AI-Assisted Development: Copilot and Friends
AI coding assistants autocomplete, explain, and refactor code inside your editor, and they change how fast you move more than how …
- Vector Embeddings Demystified
Embeddings turn text, images, or other data into lists of numbers so that similar items land close together in a high-dimensional …
- AI Literacy for IT Professionals
AI literacy for IT professionals means understanding what models can and cannot do, how to evaluate them, and how to integrate the…
- Human in the Loop: RLHF and Beyond
Human-in-the-loop systems use people to guide, correct, or approve AI behavior, and RLHF is the training technique that turns huma…
- AI Orchestration Frameworks
Orchestration frameworks chain models, tools, and data into multi-step workflows and agents. They save boilerplate and impose stru…
- Data Preprocessing for AI
Data preprocessing is the unglamorous work that determines model quality: cleaning, normalizing, chunking, and labeling the data b…
Field notes
AI is a set of capabilities, not a product, and the gap between a demo and a reliable feature is where most projects stall. The articles in this category focus on mechanism and operations: what models can do, how they learn, and the engineering discipline that makes them dependable.
Treat every AI feature as a system with inputs, outputs, costs, and failure modes. Prompts, data, evaluation, and human checkpoints matter more than model choice, and the teams that ship reliable AI are the ones that measure it.
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