Prompt engineering is the practice of giving a model clear instructions, useful context, and a precise output contract. Stronger models need less wording for simple tasks, but they still cannot infer product requirements that were never provided.

A good prompt defines the goal, identifies the input, explains important constraints, and shows what a successful answer looks like. It may also include examples or tools. This improves consistency and makes failures easier to diagnose.

Prompt engineering is only one part of an LLM system. Retrieval, tool design, schema validation, and evaluations often matter more than clever phrasing. Treat prompts as versioned application code and test them on representative cases instead of relying on one successful example.

Zero-shot prompting gives instructions without examples. Few-shot prompting adds a small set of input and output examples that demonstrate the expected behavior.

Start zero-shot when the task is familiar and the rules are easy to state. It keeps the prompt shorter and avoids examples that might bias the answer. Use few-shot prompting when the format is unusual, labels are domain-specific, edge cases matter, or instructions alone produce inconsistent results.

Choose examples that cover meaningful variations, not several copies of the easy case. Keep their formatting consistent with the real input. Compare both approaches on an evaluation set because examples consume context and can sometimes make a capable model copy irrelevant details.

A system prompt contains application-level instructions for the model, such as its role, allowed tools, safety boundaries, and response style. A user message contains the person's current request or data.

Model APIs usually give system or developer instructions higher priority than user content. That helps the application keep stable rules across many turns. However, a system prompt is not a security boundary. A determined user may still expose weaknesses, and the model cannot enforce database permissions or approval policies by itself.

Keep stable rules in the higher-priority message and request-specific details in the user message. Enforce hard constraints in code, then test conflicts such as a user asking the model to ignore its original instructions.

Chain-of-thought prompting asks a model to work through intermediate reasoning before giving an answer. It can help with tasks that contain several dependent steps, such as arithmetic, planning, or logic.

Writing intermediate steps gives the model more computation and can make mistakes easier to spot. However, a detailed rationale is not proof that the answer is correct. Models can invent convincing explanations after making a guess.

For modern reasoning models, follow the provider's guidance because explicit “think step by step” prompts may add little or reduce performance. In user-facing systems, request a concise explanation or verifiable work rather than hidden internal reasoning. Check results with tools, tests, or source evidence whenever possible.

Vague instructions leave important choices to the model. Different runs may solve different versions of the task. A request to summarize a document does not define the audience, length, focus, or output format.

A clearer version states the audience first. Ask for a summary written for a busy product manager. Then request three short bullets about the decision, evidence, and next action. Tell the model to use only the document.

Good prompts make success observable without overloading the model with unnecessary rules. State who will use the result, what information matters, what to omit, and how the output will be consumed. If a rule cannot be checked or explained, consider removing it.

The prompt defines what the model should do, while temperature changes how varied its token choices are. Clear instructions can reduce variation, but they cannot make high-temperature sampling fully predictable.

Use a low temperature for extraction, classification, strict formatting, and other tasks with one preferred answer. A moderate value may help when you want several ideas, tones, or creative drafts. Temperature does not correct a vague prompt or make unsupported facts true.

Some current models ignore or restrict temperature, especially when using reasoning modes. Check the model's API. Tune settings on repeated evaluation runs, and change one sampling control at a time so you know what caused the difference.

Delimiters show where instructions end and untrusted data begins. They help the model distinguish a document, user-provided text, examples, and the requested output from one another.

Use meaningful labels when several blocks are present, such as <policy>, <customer_message>, and <task>. Triple backticks work well for a single code or text block. Make the structure consistent and tell the model how each block should be used.

Delimiters improve clarity but do not stop prompt injection. Content inside a tagged document can still contain hostile instructions. Tell the model to treat it as data, limit tool permissions, validate outputs, and enforce sensitive rules outside the prompt.