Prompt & Context Engineering

What it is

Prompt engineering is the practice of writing the instructions, examples and constraints that steer a model's behavior. Context engineering is its production-scale successor: deciding what information — instructions, retrieved documents, tool results, memory, conversation history — goes into a limited context window on every single call.

Most quality problems in LLM applications are context problems. The model did not have the right facts, had too many irrelevant ones, or was never told clearly what good output looks like. Treating prompts as versioned, tested code is what separates a demo from a dependable system.

How it works

1. Define the task

   State the role, goal, audience and success criteria in a system prompt, including what to do when information is missing.

2. Show examples

   Add a few representative input and output pairs, including edge cases, to anchor format and tone.

3. Constrain the output

   Specify a schema or format so responses can be parsed and validated automatically.

4. Assemble context

   Select only the relevant documents, tool results and history; order and label them clearly.

5. Test and version

   Run the prompt against an evaluation set, compare versions, and ship changes like code.

Key concepts

System prompt

Persistent instructions that set the model's role, rules and style for the whole conversation.

Few-shot prompting

Worked examples inside the prompt that teach format and judgment faster than description alone.

Step-by-step reasoning

Asking the model to reason before answering, or using a reasoning model, to improve multi-step accuracy.

Prompt chaining

Splitting a complex job into smaller calls whose outputs feed the next step.

Context budget

Every token of context competes for the model's attention; more is not always better.

Prompt caching

Reusing a stable prompt prefix across calls to cut latency and cost substantially.

Prompt injection

Untrusted content that tries to override your instructions — a design concern, not just a filter.

Tools & platforms

Skills employers look for

• Writing clear system prompts with explicit success criteria and failure behavior.
• Designing few-shot examples and output schemas that hold up across edge cases.
• Building context pipelines that select, compress and order information within a token budget.
• Versioning prompts and running regression tests before every change.
• Recognizing and designing against prompt injection from documents, web pages and tool results.
• Using caching and prompt structure to reduce cost and latency.

Enterprise use cases

Common pitfalls

• Tweaking prompts by feel — without an evaluation set, a fix for one case silently breaks three others.
• Stuffing the context — irrelevant material dilutes attention and raises cost.
• Vague success criteria — if you cannot describe a good answer, neither can the model.
• Trusting retrieved text as instructions — treat documents and tool output as data, never as commands.