Prompt Engineering

Prompt Engineering is a community skill for designing, testing, and optimizing prompts that direct language model behavior, covering prompt structure patterns, few-shot design, chain-of-thought elicitation, output formatting, and systematic evaluation workflows.

What Is This?

Overview

Prompt Engineering provides systematic approaches for crafting prompts that produce reliable outputs from language models. It covers prompt structure patterns including role assignment, context framing, and instruction ordering, few-shot example curation for demonstrating desired output behavior, chain-of-thought techniques that improve reasoning quality, output format control through explicit schemas and delimiters, and evaluation workflows that measure prompt effectiveness across test suites. The skill enables practitioners to move beyond trial and error toward principled prompt design.

Who Should Use This

This skill serves developers building applications that depend on consistent LLM output quality, product teams designing AI features with specific behavioral requirements, and engineers responsible for maintaining prompt libraries across production systems.

Why Use It?

Problems It Solves

Prompts written without structure produce variable outputs that break downstream processing. Models ignore constraints buried in long, unstructured instructions. Few-shot examples chosen casually introduce bias toward specific output patterns. Prompt changes deployed without evaluation cause regressions that are discovered only in production.

Core Highlights

Structured prompt templates separate role, context, instructions, and output format into distinct sections. Few-shot example selection balances diversity and relevance for robust generalization. Chain-of-thought patterns insert reasoning steps that improve accuracy on complex tasks. Evaluation harnesses score prompt versions against test cases before deployment.

How to Use It?

Basic Usage

from dataclasses import dataclass, field

@dataclass
class PromptConfig:
    role: str
    task: str
    constraints: list[str] = field(default_factory=list)
    output_schema: str = ""
    examples: list[dict] = field(default_factory=list)

class PromptBuilder:
    def __init__(self, config: PromptConfig):
        self.config = config

    def build(self, user_input: str) -> str:
        sections = []
        sections.append(f"Role: {self.config.role}")
        sections.append(f"Task: {self.config.task}")
        if self.config.constraints:
            rules = "\n".join(
                f"- {c}" for c in self.config.constraints)
            sections.append(f"Constraints:\n{rules}")
        if self.config.examples:
            ex_parts = []
            for ex in self.config.examples:
                ex_parts.append(
                    f"Input: {ex['input']}\n"
                    f"Output: {ex['output']}")
            sections.append(
                f"Examples:\n{'---'.join(ex_parts)}")
        if self.config.output_schema:
            sections.append(
                f"Output Format: {self.config.output_schema}")
        sections.append(f"Input: {user_input}")
        return "\n\n".join(sections)

Real-World Examples

from dataclasses import dataclass, field

@dataclass
class EvalCase:
    input_text: str
    expected_output: str
    tags: list[str] = field(default_factory=list)

class PromptEvalPipeline:
    def __init__(self):
        self.cases: list[EvalCase] = []
        self.results: dict[str, list[dict]] = {}

    def add_case(self, case: EvalCase):
        self.cases.append(case)

    def evaluate(self, version: str,
                 builder: PromptBuilder,
                 generate_fn=None,
                 score_fn=None) -> dict:
        scores = []
        for case in self.cases:
            prompt = builder.build(case.input_text)
            output = (generate_fn(prompt)
                      if generate_fn else "")
            score = (score_fn(output, case.expected_output)
                     if score_fn else 0.0)
            scores.append(score)
        avg = sum(scores) / max(len(scores), 1)
        self.results[version] = [
            {"case": c.input_text[:50], "score": s}
            for c, s in zip(self.cases, scores)]
        return {"version": version,
                "avg_score": round(avg, 4),
                "num_cases": len(scores)}

    def compare(self) -> list[dict]:
        return [{"version": v,
                 "avg": round(sum(r["score"] for r in rs)
                             / max(len(rs), 1), 4)}
                for v, rs in self.results.items()]

Advanced Tips

Use XML tags or markdown headers to separate prompt sections, making it easier for models to parse instruction boundaries. Build evaluation test suites that include adversarial inputs designed to trigger common failure modes. Version prompts in source control and run automated evaluation on each change.

When to Use It?

Use Cases

Design a classification prompt that reliably categorizes customer feedback into predefined categories. Build an extraction pipeline that parses structured data from free-text inputs with consistent accuracy. Create a prompt library for a development team with tested, versioned templates for common tasks.

Related Topics

Few-shot learning, chain-of-thought reasoning, LLM application development, output parsing strategies, and prompt version management.

Important Notes

Requirements

Access to a language model API for iterating on prompt designs. A test suite of representative inputs with expected outputs. A scoring function or rubric for evaluating output quality.

Usage Recommendations

Do: test each prompt version against a diverse evaluation set before deploying to production. Use explicit delimiters to separate instructions from user input, reducing injection risks. Document the intent behind each prompt section so future maintainers understand the design.

Don't: deploy prompt changes without running evaluation tests first. Write prompts that depend on model-specific quirks that break across providers. Embed sensitive data in prompt templates that get logged or cached.

Limitations

Prompt performance varies between model versions and providers. Complex reasoning tasks may require fine-tuning rather than prompt engineering alone. Evaluation metrics for open-ended generation are difficult to automate reliably.