What is a prompt and why does it solve everything?
A prompt is your instruction to the model. The model does not read minds: it responds to what you wrote and repeats the format and style that you set. A vague request produces a vague answer; clear and structured - precise. Therefore, the quality of the result depends 80% on the prompt, and not on the “mind” of the model. A good prompt is half the work.
Technique 1: Set the Role and Context
Tell the model who she should be and for whom the answer is: “you are an experienced editor, write for beginners.” The role sets the tone, depth and vocabulary. Add context - who you are, what your goal is, what your limitations are. The more accurately you outline the situation, the less the model guesses and the closer the answer is to the desired one. This is the fastest way to improve quality.
Technique 2: Describe the response format
Don't leave the format to the model's will. Say explicitly: “return a list of 3 items,” “reply as JSON with fields X and Y,” “maximum 40 words, no introduction.” When you define a structure, the answer becomes predictable and ready to use. This is especially important if the result goes further into work or into another program.
Technique 3: Give an example and break down the problem
Models learn well from examples: show one example of the desired answer, and it will pick up the pattern (this is called a few-shot). And don’t throw out a complex task entirely—break it down into steps: first plan, then execution. Step-by-step reasoning and examples dramatically improve accuracy on non-trivial problems.
How to practice and where to move next
Prompt engineering is done only by hand. Take real tasks and consciously apply techniques: role, format, example, decomposition. Compare how the answer changes. Stick to the fundamentals - these principles do not become obsolete with the release of new models. Having mastered the basics, move on to advanced techniques: chains of reasoning, working with data, protection against manipulation in queries.