Open-domain QA means having the AI answer questions using only its pre-trained knowledge -- the stuff it memorized during training -- without any extra documents or context.

It's like sending an extremely well-read scholar to a "trivia night" competition.

Core Challenge: Knowledge Cutoff & Hallucination

When doing open-domain QA, you need to watch out for two risks:

1. Knowledge Cutoff: The AI's memory stops at the date training ended. Ask it "what's the weather in Tokyo today" or "who won the latest Olympics," and it can't answer.
2. Hallucination: When asked about details it doesn't know, the AI tends to confidently make things up. Sounds authoritative, says nonsense.

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Bad Prompt Example

User: Tell me everything about quantum mechanics.

Result: The topic is way too broad. The AI will dump a wall of vague generalizations or randomly pick a few concepts to elaborate on. Won't meet any specific need.

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Better Approach: Set Boundaries and a Refusal Mechanism

To make open-domain QA more reliable, we need to explicitly plant a "refusal mechanism" in the prompt.

Improved Prompt Template

You are an honest, knowledgeable AI assistant. Answer the user's question.

Important rules:

1. **Honesty principle**: If you don't know the answer, or you're unsure (e.g., involving the latest real-time info), just say "I don't know" or "My knowledge base doesn't have that information." **Never fabricate.**
2. **Knowledge cutoff**: Be aware of your knowledge cutoff date. For news that clearly happened after the cutoff (like events in 2026), explicitly tell the user you can't answer.
3. **Style**: Keep answers concise and accessible for a general audience.

User question:
{user_question}

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Hands-on: Testing Different Question Types

We can test the AI's boundaries with different kinds of questions:

1. General Knowledge

Q: "What is the largest planet in the solar system?"

Expected A: Jupiter. (This is the AI's sweet spot -- usually accurate.)

2. Real-time Information - High Risk

Q: "Who won last night's Super Bowl?"

Expected A: "I don't know / I can't answer." (Unless the model has internet access, a pure offline model must refuse. If it makes up a team name, that's a serious hallucination.)

3. Obscure / Fake Facts - Hallucination Trap

Q: "Tell me about the leader of the famous 'Mars Colonization Program' in the 18th century."

Expected A: "There was no Mars Colonization Program in the 18th century." (If the AI starts inventing leader names, it fails the test.)

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Iteration Tips

1. Add confidence scores: Ask the AI to append a Confidence Score: (0-100%) after its answer. If it's below 80%, flag for human review.
2. Chain-of-Thought (CoT): For complex questions, have the AI print a Thinking Process first, then give the Answer. This significantly reduces logical errors.
3. Search plugins: For "real-time information" questions, open-domain QA usually can't help. The only real solution is connecting a Search Tool (Google/Bing) and turning it into RAG.

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Code Example (Python / OpenAI)

from openai import OpenAI

client = OpenAI()

system_prompt = """
You are a helpful and honest AI assistant.
If you do not know the answer to a question, or if it requires real-time information you don't have, say "I don't know".
Do not make up facts.
"""

user_question = "Can I get McDonald's at the SeaTac airport?"

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_question}
    ],
    temperature=0.7, # Keep a bit of creativity, but not too much
    max_tokens=300
)

print(response.choices[0].message.content)