Prompt Engineering

Artificial Intelligence 09 Sep 2026 8 min read

Contextual Calibration for Few-Shot Classifiers

A language model can behave like a classifier without any parameter updates: give it a few labeled examples, present a new input, and ask it to choose a label. The surprising problem is that the answer can depend not only on the new input, but also on details such as the prompt wording, demonstration order, and label tokens. That creates a practical debugging trap. A prompt may appear to teach the task while also giving the model a baseline preference for one answer before meaningful input is considered.

Artificial Intelligence LLM

Artificial Intelligence 03 Sep 2026 6 min read

Use Few-Shot Prompting with Effective Examples

A prompt can explain a task with instructions, but sometimes examples communicate the desired behavior more precisely. Few-shot prompting places a small number of input-output demonstrations in the model’s context before the real input. This technique is useful when a task has a specific output format, subtle classification boundary, naming convention, or transformation rule that is difficult to describe completely in prose. The model is not retrained by these examples. Instead, it uses the demonstrations as part of the current context when generating the next response.

Artificial Intelligence LLM

Artificial Intelligence 02 Sep 2026 5 min read

Control LLM Randomness with Temperature and Top-p

Large language models usually generate text one token at a time. At each step, the model assigns scores to possible next tokens, those scores become probabilities, and a decoding strategy chooses what comes next. Two common controls in that process are temperature and top-p. They are often described as creativity settings, but that description is incomplete. They change how the model samples from its probability distribution, which affects repeatability, diversity, and the chance of selecting lower-probability tokens.

Artificial Intelligence LLM

Artificial Intelligence 02 Sep 2026 5 min read

Budget LLM Context Windows Without Losing Critical Instructions

Large-language-model applications rarely fail because a prompt is one token too long. They fail because context growth is handled without priorities. Chat history expands, retrieval returns more passages, tool results become verbose, and eventually the application truncates whichever text happens to be easiest to cut. A safer design treats the context window as a budget with explicit allocations. The goal is not to fill every available token. The goal is to preserve the information that controls behavior while leaving enough room for a complete answer.

Artificial Intelligence LLM