Prompt Engineering
Quick Answer
Prompt engineering is the practice of designing the text instructions given to a language model to produce reliable, accurate and appropriate outputs. Good prompts unlock significantly better performance without any change to the underlying model.
In Depth
What Prompt Engineering really means
Effective prompts specify the task, the desired format, the tone, constraints, and ideally one or more examples. Techniques such as chain-of-thought prompting, role prompting and structured output formatting are now core professional skills for anyone deploying LLMs.
Prompt engineering is both an engineering discipline and a writing craft. Small wording changes can produce large swings in output quality, which is why UK teams increasingly treat prompts as versioned, tested artefacts rather than ad-hoc strings.
Why It Matters
Business relevance for UK organisations
Every commercial LLM deployment depends on well-engineered prompts. Investing in prompt design, testing and documentation typically yields bigger quality gains than upgrading to a more expensive model.
Real-world example
How this shows up in practice
A London recruitment firm improved CV-screening accuracy by 28% simply by restructuring its prompts to include explicit grading criteria and worked examples.
Related Terms
Continue exploring
Large Language Model (LLM)
A Large Language Model (LLM) is a type of neural network trained on vast quantities of text to understand and generate human language. LLMs power chatbots, copilots, content generators and many modern AI features across consumer and business software.
AdvancedPrompt Injection
Prompt injection is a class of security vulnerability where a malicious input manipulates an LLM into ignoring its instructions, leaking confidential information, or taking unintended actions. It is the most important emerging AI security risk for organisations.
AdvancedFew-shot Learning
Few-shot learning is when a model learns a new task from a small number of examples, often supplied directly in the prompt. It sits between zero-shot (no examples) and full fine-tuning (many examples) and offers an excellent balance of quality and effort.
TechnicalHallucination
A hallucination is when an AI model produces output that sounds plausible but is factually incorrect, fabricated or inconsistent with its sources. Hallucinations are a fundamental property of current generative models and the single biggest risk in enterprise deployments.
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