Question 1

What is prompt engineering?

Accepted Answer

Prompt engineering is the practice of designing, structuring, and optimizing inputs (prompts) to ensure large language models produce accurate, reliable, and properly formatted outputs.

Question 2

Why can't we just write basic prompts?

Accepted Answer

Basic prompts often lead to hallucinations, inconsistent formats, or off-brand responses in production. Prompt engineering builds structured systems that handle edge cases and guarantee stable outputs.

Question 3

What is Chain-of-Thought prompting?

Accepted Answer

Chain-of-Thought prompting directs the LLM to write down its step-by-step reasoning before outputting the final answer, which drastically improves accuracy on complex logical or mathematical tasks.

Question 4

How do you protect against prompt injection attacks?

Accepted Answer

We set up input sanitization layers, define strict system instructions separating user input from system rules, and implement output guardrails to check for leaked instructions.

Question 5

How do you test prompt changes before launching?

Accepted Answer

We use framework tools like PromptFoo to run automated tests across hundreds of test cases, comparing the new prompt's outputs against the old prompt's results to prevent regressions.

Question 6

Can prompt engineering reduce API token costs?

Accepted Answer

Yes, we structure prompts to be concise, remove redundant instructions, use prompt caching, and implement semantic caching to prevent querying the LLM for identical questions.

Question 7

Which LLM models do you support?

Accepted Answer

We support all major models, including OpenAI GPT-4, Anthropic Claude 3, Google Gemini, and open-source models like Llama 3 and Mistral, tuning prompts specifically to each model's strengths.

Question 8

What are output guardrails?

Accepted Answer

Output guardrails are verification layers (using tools like Guardrails AI) that check model outputs against specific rules, ensuring they are valid JSON, free of inappropriate content, and factually correct.

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