Prompt Engineering
The practice of designing the instructions, examples and context given to an LLM so it produces reliable, well-formatted results for a specific task.
Definition
An LLM does what its input suggests, so the input is the main control surface. Prompt engineering is the discipline of writing that input well: a clear role and goal, explicit rules, the format of the expected output, the context the model needs and, often, a few worked examples. In an application the prompt is a template filled at run time with user input, retrieved documents and tool results, rather than a sentence typed into a chat box.
Established techniques include few-shot examples (show, do not tell), asking the model to reason step by step before answering, constraining output with schemas or function calling, separating instructions from untrusted data with clear delimiters, and telling the model what to do when it cannot answer. Prompt engineering is usually the first lever to pull, before RAG for knowledge or fine-tuning for behaviour, and it is only trustworthy when paired with evals.
Example: a prompt for ticket triage states the five allowed categories with one example each, requires a JSON object with "category" and "confidence", and instructs the model to use "unclear" below a confidence threshold. Accuracy rises and the output plugs straight into the ticketing system.
What it means in practice
- It is the cheapest and fastest way to improve an LLM feature; most quality problems are prompt problems first.
- Prompts encode business rules and tone, so they need ownership, review and version control like code.
- Long prompts cost tokens on every request; clarity and brevity pay twice.
- Prompts are not a security boundary; policy must also be enforced by guardrails.
How Teksmobile uses it
Prompt design, versioning and evaluation are part of every LLM application development engagement, and our AI chatbot work lives or dies by it. When to stop prompting and start tuning is covered in our guide prompt engineering vs fine-tuning.
Related terms
Part of the Teksmobile AI Glossary. For longer reads, see our guides.
Frequently asked questions
Is prompt engineering still needed with better models?
Yes, though it has shifted from tricks to clear specification. Newer models follow instructions better, which makes a precise, well-structured prompt more valuable than before.
What is a system prompt?
The standing instructions an application sends with every request: role, rules, format, tone and constraints. Users do not see it, but it shapes every answer.
Should prompts be version-controlled?
Always. Prompts are code. Store them in the repository, review changes, and run evals before deploying a new version.