What is Prompt Engineering?
Turkish: Prompt Engineering
Prompt engineering designs instructions, context, examples, and constraints so language models produce more useful, consistent, and reviewable output.
What is Prompt Engineering?
Prompt engineering is the deliberate design of instructions given to an AI model, including goal, context, data, constraints, and output format. The purpose is not to “trick” the model; it is to reduce misunderstanding, missing assumptions, and uncontrolled output.
A strong prompt often includes a role, task, source material, exclusions, example output, and evaluation criteria. One-line prompts can be enough for simple work; for support summaries, proposal drafts, or data classification, structure matters more.
Techniques
- Context injection: Provide the information the model should use.
- Few-shot examples: Demonstrate the expected behavior with a few examples.
- Output schema: Specify JSON, table, or bullet structure.
- Control questions: Ask the model to surface uncertainty or cite its source material.
Business Use
Prompt engineering is used to classify support tickets, draft proposals, summarize meeting notes, create product descriptions, and answer from internal documents. Quality does not depend on the prompt alone; privacy controls, test sets, human review, and logging also matter.
Prompting may be enough to guide LLM behavior; when answers depend on company knowledge, RAG usually provides a more reliable foundation.
Related Terms
An AI agent is a software component that uses an LLM, tools, and data sources to plan steps and complete a defined goal.
LLM (Large Language Model)An LLM is a model trained on large text datasets that can understand and generate natural language, forming the basis of tools like ChatGPT.
Prompt InjectionPrompt injection is an attack where user or external content tries to override hidden instructions and steer an AI model.
RAG (Retrieval-Augmented Generation)RAG is an AI architecture where a language model retrieves relevant passages from documents or databases before generating an answer.