What is Prompt Engineering?

You ask the LLM a question. The answer is wrong. You rephrase slightly. Now it's right.

This isn't magic. It's the nature of language models. LLMs predict statistically likely continuations of text. The specific words, structure, and context you provide directly shape what continuation the model predicts. Prompt engineering is the discipline of crafting those inputs deliberately.

Why it matters: In production AI, you can't hope prompts work. You need them to work reliably across diverse inputs, edge cases, and user behaviors. Good prompt engineering is the difference between a demo that impresses and a product that delivers.

Core Prompting Techniques

Zero-Shot Prompting

Give the model a task with no examples. Relies entirely on the model's pre-trained knowledge.

Classify the following customer message as positive, negative, or neutral:
"Your product arrived damaged and support hasn't responded in three days."

Works well for straightforward tasks where the model has strong pre-training on similar content. Fails when the task requires domain-specific interpretation or unusual formats.

Few-Shot Learning

Provide examples that demonstrate the desired behavior.

Classify customer messages:

Message: "Love this product, works perfectly!"
Classification: positive

Message: "It's okay, does what it says."
Classification: neutral

Message: "Your product arrived damaged and support hasn't responded in three days."
Classification:

Few-shot examples establish patterns the model should follow. Particularly useful for:

• Domain-specific terminology or categories
• Unusual output formats
• Edge cases that need explicit guidance

Chain-of-Thought Prompting

Ask the model to show its reasoning before giving the answer.

Solve this step by step:
A customer purchased 3 items at $45 each with a 20% discount applied to the total. What did they pay?

Chain-of-thought improves accuracy on complex reasoning tasks and provides transparency into how the model arrived at its answer. The reasoning may not reflect true internal processing, but it often produces better outputs than direct answers.

Role Assignment

Define a persona or role for the model to adopt.

You are a senior compliance officer reviewing AI system documentation for regulatory adherence. Analyze the following deployment plan and identify potential compliance gaps.

Roles can invoke specific knowledge, adjust tone, and frame the task appropriately. Useful for specialized domains where generic responses fall short.

Output Formatting

Specify the exact format you need.

Extract the following information from this contract and return as JSON:
- parties: list of party names
- effective_date: date in YYYY-MM-DD format
- term_length: duration in months
- termination_clause: boolean, true if termination clause exists

Explicit formatting reduces parsing errors and integration friction. Be specific. "Return as JSON" is less reliable than showing the exact structure expected.

Iterative Prompt Development

Prompts aren't written. They're developed through iteration.

Step 1: Start Simple

Begin with a basic prompt that captures the core task. Don't over-engineer initially.

Step 2: Test with Diverse Inputs

Try edge cases, adversarial inputs, and realistic variation. Where does the prompt fail?

Step 3: Analyze Failures

Categorize failures:

• Misunderstanding the task
• Missing context or knowledge
• Wrong format or structure
• Hallucinations or fabrications

Step 4: Refine Targeted Constraints

Add instructions that address specific failure modes:

• "If you don't know, say so" (for hallucinations)
• "Consider only the provided context" (for grounding)
• "Follow this exact format" (for structure)

Step 5: Iterate

Repeat until quality meets requirements. Document what worked, what didn't, and why.

Domain-Specific Prompt Engineering

Generic prompts produce generic results. Effective production prompts are tailored to:

Domain Terminology

Include or define terms specific to your domain. Don't assume the model interprets specialized vocabulary correctly.

Expected Edge Cases

Explicitly handle known edge cases in prompt instructions rather than hoping the model figures them out.

User Patterns

Design prompts around how your actual users phrase requests, not idealized input. Real users misspell, use abbreviations, and ask ambiguous questions.

Business Logic

Encode business rules that the model shouldn't violate: pricing constraints, policy limitations, compliance requirements.

Prompt Engineering for RAG

Retrieval-augmented generation introduces specific prompting challenges:

Grounding Instructions

Explicitly instruct the model to base answers on provided context:

Answer the following question using ONLY the information in the provided documents. If the documents don't contain the answer, say "I don't have information about that."

Documents:
{retrieved_documents}

Question: {user_question}

Source Attribution

Request citations to specific documents:

Include references to specific documents that support your answer in the format [Doc N].

Handling Contradictions

Guide behavior when retrieved documents conflict:

If the documents contain conflicting information, note the discrepancy and provide the most recent or authoritative answer.

Common Pitfalls

Over-Prompting

Adding too many instructions can confuse the model or cause it to fixate on constraints at the expense of the core task. Start minimal, add only what's needed.

Instruction Following Failure

LLMs sometimes ignore instructions, especially when they conflict with strong pre-training patterns. Test that constraints actually constrain.

Prompt Injection Vulnerability

User input included in prompts can manipulate model behavior. Separate user input from instructions, validate and sanitize inputs, and use guardrails to catch manipulation attempts.

Assuming Stability

The same prompt may behave differently across model versions, temperature settings, or even due to non-determinism. Test prompts under realistic production conditions.

Measuring Prompt Quality

Prompts need evaluation like any other code:

Accuracy: Does the prompt produce correct outputs? Consistency: Does it perform reliably across inputs? Robustness: Does it handle edge cases and adversarial input? Efficiency: Is the prompt as concise as possible while maintaining quality?

Track these metrics across prompt versions to understand what changes improve or degrade performance.

How Swept AI Supports Prompt Engineering

Prompt quality directly impacts AI system behavior. Swept AI helps ensure prompts perform as intended:

• Evaluate: Test prompts against diverse input distributions before deployment. Identify failure modes, edge cases, and inconsistent behavior across your actual use case.

• Supervise: Monitor prompt performance in production. Track when prompts produce unexpected outputs, fail to follow constraints, or drift in quality over time.

• AI guardrails: Enforce constraints that prompts alone can't guarantee. Catch prompt injection attempts, ensure compliance with policies, and validate outputs regardless of prompt variability.

Prompts are the interface between your intent and model behavior. AI supervision ensures that interface works reliably in the real world.