Editorial desk
Core AI concepts without the jargon or inflated claims.
01 · Explainer
A language model predicts and generates sequences from learned patterns, but the product people use also includes instructions, retrieval, tools, filters, memory, user data, and an interface.
7 min read
02 · Explainer
A large context limit says how much material can fit into a request and response under defined conditions. It does not guarantee extraction, retrieval, recall, or correct use of every detail.
8 min read
03 · Explainer
RAG retrieves outside material and places selected passages into the model’s context. It can improve freshness and traceability while introducing extraction, ranking, permission, and citation failures.
9 min read
04 · Explainer
An agent pursues a goal across steps and can select or use tools under some degree of autonomy. The important questions are what it can observe, decide, remember, and change.
10 min read
05 · Explainer
Training learns model parameters, inference generates an output, fine-tuning further adjusts a model, and retrieval supplies outside information at runtime. Mixing the terms causes bad privacy and capability assumptions.
7 min read
06 · Explainer
A benchmark defines tasks, data, scoring, prompts, tools, attempts, and aggregation. Change the setup and the score can change even when the model name does not.
8 min read
07 · Explainer
An answer can invent a fact, misquote a source, follow a false premise, confuse entities, use stale information, or express uncertainty poorly. Each failure needs a different fix.
9 min read
08 · Explainer
Embeddings represent items as vectors so systems can compare patterns and retrieve related material. Similarity can be useful without guaranteeing truth, intent, fairness, or permission.
10 min read