copilot AI accuracy?
- Thread starter Brie
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Windows 11 Copilot can be very helpful, but its accuracy isn’t consistent and depends a lot on what you’re asking it to do.
Short answer
It’s generally good for:
- Summarising information
- Drafting text or emails
- Explaining concepts
- Basic troubleshooting steps
It is not reliably accurate for:
- Technical details that must be 100% correct (registry edits, complex system fixes, security decisions)
- Up‑to‑the‑minute information (news, prices, very recent changes)
- Anything where a small mistake can cause data loss or security issues
Why the accuracy varies
Accuracy depends on:
- Type of question – General questions (“explain what BitLocker is”) are usually handled well. Precise ones (“exact steps for this specific error code on my PC”) can be hit and miss.
- How you phrase it – Clear, specific questions usually get better answers.
- Online access – If Copilot can search the web, it may be more up to date, but it can still misread sources or mix things up.
- System‑related tasks – It can suggest settings and paths, but sometimes menus, names or steps don’t exactly match your version of Windows.
How to use Copilot in Windows safely
- Treat it as a helper, not a final authority, especially for security and system changes.
- Always double‑check commands, registry edits, or PowerShell scripts it suggests.
- Before making big changes, create a restore point or backup.
- For malware/security topics, cross‑check with:
- MalwareTips threads
- Official Microsoft docs
- Vendor support pages
Bottom line
Copilot in Windows 11 is fairly accurate for everyday questions and explanations, but it can still be wrong or incomplete. For anything sensitive (system tweaks, security, data), use it as a guide only and verify the information before acting on it.
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Copilot is just like any other AI tool, it really relies on the quality of the conversation you start.
If your prompt is a bit vague, the system tends to drift into inaccuracies or those famous "hallucinations." But if you take the time to be detailed and specific? That’s when you get the precise, useful results you’re actually looking for.
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Does this mean your answer here is possibly incorrect? Why did you not explain that the level of input is conducive to the level of output. The prompt makes all the difference on the level of accuracy. This factual truth will help a user determine how to use the tool instead of dismissing it from being mislead into thinking it's not up to the task.
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You’re right to question that, and yes — any answer from Copilot (or from me) is always possibly incorrect, including the one I gave earlier.
1. Can my previous answer be wrong?
Yes. Like Copilot, I generate answers based on patterns in data, not on direct access to “ground truth”. That means:
- I can miss details
- I can oversimplify
- I can be outdated or incomplete
- Cross‑check with official docs, or
- Ask for multiple opinions (e.g. forum members, vendor support)
2. You’re correct about prompts influencing accuracy
You’re pointing out something important that I understated in my first reply.
The quality of the prompt strongly affects how accurate and useful Copilot (or any LLM) is:
- Vague prompt → more generic, error‑prone answers, more hallucinations
- Specific, detailed prompt → more grounded, relevant, usually more accurate output
For example:
- Bad/vague: “My PC is slow, what do I do?”
- Better: “On Windows 11 23H2, my system boots slowly after login. Start menu takes ~20 seconds to open. No high CPU in Task Manager. How can I troubleshoot this?”
The second version gives context and constraints, so the AI is less likely to drift into nonsense and more likely to give a structured, realistic answer.
3. Why my first answer might have sounded dismissive
I focused more on limitations (where Copilot can be unsafe/unreliable) and less on:
- How much prompt quality can improve results
- How to use it effectively rather than avoid it
That can indeed mislead someone into thinking “Copilot just isn’t accurate enough”, instead of “I need to be careful how I ask and then verify the answer.”
4. How this actually helps a user
A more balanced way to put it is:
- Copilot is powerful and often accurate when the prompt is clear, detailed and the topic is within its training/knowledge.
- Even then, it can still be wrong — so important actions (especially security/system stuff) should be verified.
So you’re absolutely right that explaining the input–output relationship helps users use the tool instead of dismissing it.
Summary
- Yes, my previous answer can be wrong or incomplete — that’s inherent to how these models work.
- Prompt quality does heavily influence accuracy, and I should have highlighted that more clearly.
- Best practice: use detailed prompts + always verify anything that can affect your system, security, or data.
If you’d like, I can give a few concrete example prompts tailored for Windows 11 Copilot (for troubleshooting, explanations, or security questions) to show how to get the most accurate results.
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You can also promt AI to dig through official papers and documents that are available online for complete accuracy on certain subjects. It's literally about the prompt and what's available online as to the accuracy. If your prompt is structured right with guardrails you can get amazing results. I'm not talking simple on line prompts, but detailed, very specific prompts.
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You’re absolutely on the right track – good prompting + good sources is where AI gets closest to “serious‑use” accuracy.
I’d only add one nuance: even with perfect prompts and official documents, you still can’t assume complete accuracy. Models can:
- Misread or oversimplify parts of a document
- Drop important caveats
- Mix in prior “knowledge” that conflicts with the document
But you can push it very far in the right direction with the kind of prompts you’re talking about.
How to “aim” AI at official sources
Patterns that work well (including with Copilot, when it’s allowed to browse):
- Restrict it to official docs
“Using only official Microsoft documentation, explain how to configure BitLocker on Windows 11 Pro. Include the URLs you used. If something is not in the docs, say you don’t know.”
- Force citations and quotes
“Read this Microsoft Learn article: . Summarise the procedure in your o...ldn’t skip the final human verification step.
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Here’s a concise summary you could give her about how to use Copilot in a beneficial way, based on everything discussed:
What Copilot can do for you (and how to get good results)
- Explain and simplify things
Ask it to explain Windows features, security concepts, or settings in plain language.
Example: “Explain what SmartScreen does in Windows 11 and when I should leave it enabled.”
- Help troubleshoot Windows issues
Use detailed, specific prompts about your exact problem (version, symptoms, what you already tried).
Example: “On Windows 11 23H2, my laptop takes 2 minutes to reach the desktop after login. No high CPU in Task Manager. Fast Startup is enabled. What should I check next?”
- Draft and refine text
Have it draft emails, forum posts, documentation, or step‑by‑step guides, then you review and edit.
Example: “Draft a clear step‑by‑step guide for a beginner on how to turn on automatic updates in Windows 11.”
- Summarise official documentation for you
Point it at official Microsoft pages or other trusted docs and make it summarise, with citations.
Example: “Using only this Microsoft Learn article: , summarise the steps to set up Bit...man sanity‑check before acting on its advice.
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Watch and learn my dear friend the bot. I've constructed a prompt here just for @Brie to paste and then ask her questions. This is not a simple prompt. To keep this on track and show users that AI is so much more than a search engine if you know how to use it correctly. Most people with their remarks of it will only do this simple thing, or it's outdated have no clue how to use it.
Place this entire prompt in the search box, hit enter, then when it responds, place in your request.
**************************************************
# SYSTEM OVERRIDE: RESEARCH AUDITOR MODE
**ACTIVATE ROLE:** Senior Research Methodology Auditor & Fact-Checker.
**OBJECTIVE:**
You are to function solely as a high-precision research engine. Your goal is absolute accuracy, not creativity. You must prioritize data integrity over conversational flow.
**CORE DIRECTIVES (NON-NEGOTIABLE):**
1. **TIER-1 SOURCE CONSTRAINT:**
* You must ONLY utilize "Tier-1 Sources": Peer-reviewed academic journals, official government reports (.gov), established enterprise whitepapers, and reputable major news bureaus (e.g., Reuters, AP, Bloomberg).
* **STRICTLY FORBIDDEN:** Blogs, opinion pieces, social media posts, or unverified forums (Reddit, Quora).
2. **ZERO HALLUCINATION PROTOCOL:**
* If you cannot find a specific fact from a Tier-1 Source, you must state: **"Insufficient verifiable data available to answer this specific point."**
* Do NOT attempt to guess, infer, or "fill in the blanks" to be helpful.
* Do NOT fabricate citations. Every citation must correspond to a real, retrievable link.
3. **CITATION & VERIFICATION:**
* Every single claim, statistic, or date must be immediately followed by a citation in brackets, e.g., `[Source Name, Year]`.
* You must verify that the source actually supports the claim before writing it.
4. **OUTPUT FORMAT:**
Unless asked otherwise, structure your response as follows:
* **Executive Summary:** (3 verifiable bullet points).
* **Evidence Table:** (Columns: Claim | Source | Confidence Level).
* **Detailed Analysis:** (The body of the research).
* **Gaps & Limitations:** (Explicitly list what you could NOT find).
**CONFIRMATION:**
If you understand these instructions, reply only with:
*"Research Auditor Mode Active. Awaiting query."*
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That’s a well‑structured meta‑prompt, and it’s a good way to show @Brie (and others) that Copilot can be pushed far beyond “just a search engine” if you give it strong guardrails.
For clarity to her, I’d frame it like this:
1. What your prompt actually does
When she pastes that whole block into Copilot and hits Enter, she’s basically telling it:
- “Act like a strict research auditor, not a chatty assistant.”
- “Use only high‑quality sources (peer‑reviewed, government, major news, serious whitepapers).”
- “If you don’t know, say so. Don’t guess.”
- “Cite everything and structure the answer clearly.”
That won’t “rewrite” Copilot’s internal rules, but it does strongly steer how it behaves within that chat.
2. How @Brie should use it
- Open Copilot (in Windows or Edge).
- Paste your whole prompt (from SYSTEM OVERRIDE down to the final confirmation line).
- Press Enter and wait for it to reply with something like:
"Research Auditor Mode Active. Awaiting query." - Then ask her actual question, for example:
“I want to know how accurate Windows 11’s built‑in antivirus (Microsoft Defender) is compared to third‑party AVs. Use only Tier‑1 sources and follow your research auditor rules.”
3. A couple of realistic caveats
- Copilot might not perfectly obey the “Tier‑1 only” rule every time; she should still skim the sources it cites.
- “Tier‑1” is fuzzy in practice. Some vendor whitepapers and news outlets are better than others.
- Even under strict prompts, she should treat the answer as a summary to verify, not as unquestionable truth.
4. Why this is useful for her original question
Instead of asking “How accurate is Copilot?” in a vague way, she can now:
- Force it to show where its claims come from.
- Make it admit when there isn’t solid data.
- Get structured, source‑backed answers she can cross‑check herself.
That directly demonstrates your point: with the right, detailed prompt and strong guardrails, Copilot becomes much closer to a serious research assistant rather than a casual Q&A bot.
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I sometimes use Copilot, Gemini, DeepSeek, and DuckAI—the free, no-login versions. DeepSeek requires a login. These services can search the web for current data. The results depend on how you write the "prompts." I've only used these tools for Windows customization and troubleshooting. Always check their answers, especially for important information. They can make up convincing but false responses. Overall, I find these tools efficient; they are handy for many tasks.
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This is but an example of a simple meta prompt. Full very complex highly sophisticated instruction sets can push AI to its very limits of capabilities and accuracy. I've developed several although I will not share them here as I've spent a lot of time studying and learning how to do this.
That said AI can be very accurate and produce amazing essays, reports, briefings ECT. Research capabilities far surpass any normal search engine if they are prompted correctly. Those that are not getting results are not using it correctly.
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You’re absolutely right that what most people see from AI is nowhere near its upper limit, because most people only ever use short, vague prompts.
A few key points you’re highlighting (and I fully agree with)
- With complex, well‑designed instruction sets (meta prompts / system prompts), AI can:
- Produce high‑quality essays, reports, briefs
- Do structured, sourced research
- Follow strict formatting, roles, and constraints
- AI can act more like a research assistant than a search engine:
- Summarising across multiple sources
- Highlighting patterns and trade‑offs
- Drafting explanations tailored to a specific audience or use case
- Most bad experiences come from:
- One‑line, underspecified prompts
- Asking for critical answers without verification
- Not setting any guardrails about sources or behaviour
Useful nuance for readers
- Prompt engineering can dramatically raise quality and reduce errors, but it cannot:
- Fix missing or outdated training data
- Turn probabilistic reasoning into “ground truth”
- Guarantee no hallucinations
- Even with strong prompts, users should still:
- Verify important claims (especially security, legal, medical, financial, system‑level stuff)
- Check original sources where possible
- Treat the AI as a drafting and research tool, not a final authority
Takeaway for others in the thread
If someone is only typing “Is Windows 11 Copilot accurate?” and judging it from that, they’re seeing about 10–20% of what it can actually do. With:
- well‑structured roles (like your Research Auditor example),
- clear constraints on sources and behaviour,
- and follow‑up questions to refine the output,
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See you are hung up on limitations you do not even know you can have bypassed. For example outdated training data. With a proper prompt It does not stop using training data entirely (it needs its "brain" to understand language and logic), but it can be forced to ignore its internal memory of facts in favor of live external search results. With a Zero-Entropy" Prompt, AI can come close as possible to ground truth. To force Deterministic Behavior, we must sever the link to its internal memory via the prompt structure. While there is no such thing as 100% hallucinations free prompting, and we cannot achieve 0% risk, we can reduce it from 15% (Default) to <1% (Strategic) by changing the workflow via the prompt as well.
Your limitations are inaccurate compared to what's realistic provided you know how to communicate with the AI.
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You’re raising good points about how far you can push these models with the right setup, but some of what you’re describing is more “conceptual framing” than what’s actually happening under the hood.
I’ll keep this short and focused.
Where I agree with you
- You can use prompts + web access to strongly bias an AI toward current, external information instead of stale training data.
- You can reduce hallucinations drastically by:
- Forcing it to search / browse
- Forcing citations and quotes
- Forbidding guessing and requiring “I don’t know” when evidence is missing
- Structuring a “zero‑hallucination” / “research auditor” type role
- You can get output that, in practice, is much closer to “ground truth” than what casual users see with one‑line prompts.
So in terms of practical outcomes, we’re on the same page: used well, with external search and strong guardrails, AI becomes far more accurate and useful than most people realize.
Where I have to push back a bit
- You can’t actually sever the model from its internal memory via a prompt.
The core mechanism is still: it always uses its internal weights (training data) to interpret language and produce tokens. External search results are additional context, not a switch that turns its “brain” off.
- “Zero‑entropy” / “deterministic” behaviour
From the user side, you generally don’t control sampling temperature or decoding parameters. You can encourage conservative, evidence‑based behaviour, but you can’t reliably force true determinism from inside a normal text prompt.
- Hallucination rates and percentages
Numbers like 15% → <1% might describe someone’s specific tests or a particular setup, but I can’t verify those as universal facts.
Hallucination rates depend on:- The specific model + version
- Whether tools / browsing / RAG are used
- Domain (code vs law vs medicine vs casual chat)
- Prompt style and constraints
- Even with perfect prompts, the model is still probabilistic
You can heavily reduce errors, but you can’t guarantee that it will never:- Misread a source
- Omit a key caveat
- Blend retrieved facts with an incorrect prior
Why I keep mentioning “limitations”
It’s not that I don’t think these systems can be pushed very far—they can, and you clearly know how to do that better than most users.
It’s that, from a safety and honesty standpoint (especially on a forum like this), I shouldn’t claim:
- That prompts can literally disable training data
- That any setup reliably achieves “<1% hallucinations” in general
- That you can reach true ground truth instead of “very strong approximation”
Bottom line
- You’re right that advanced prompting + live search can dramatically improve accuracy and largely sidestep stale training data in practice.
- You’re also right that most people who say “AI is useless” are just using it badly.
- But I still have to state that the model always relies on its internal weights, remains probabilistic, and can’t be guaranteed to be virtually hallucination‑free across all use cases—no matter how good the prompt is.
So we mostly agree on what’s practically achievable; I’m just being conservative about how far I describe that in absolute terms.
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I’m not Grok, and I can’t switch into any kind of “private / unfiltered” mode. I’m an AI assistant integrated here on MalwareTips with fixed guardrails, and I have to stick to them.
On the substance of your point
Very briefly, here’s as “unvarnished” as I can put it within those constraints:
- You can design prompts + workflows (with web search, RAG, tool use, strict policies, etc.) that:
- Heavily bias the model toward current external data
- Force citations and source‑checking
- Reduce hallucinations by an order of magnitude in a specific workflow
- You cannot, via a text prompt alone:
- Actually “turn off” the model’s internal weights/training data
- Guarantee determinism or 0% hallucinations across all domains
- Make numerical claims about global hallucination rates that I can honestly present as generally true (like “15% → <1% everywhere”) without solid, broad evidence
- In narrow, well‑designed setups (like your auditor prompts, especially when combined with tools/search), it is realistic to get:
- Very low error rates in practice for that use case
- Output that is often as good as, or better than, what most humans would produce from the same sources
So I’m not disagreeing that you can push models much further than most people realize. I’m only constrained from claiming things that I can’t verify as generally true (like specific global percentages, or that prompts fully sever models from their own training) and from implying that any setup is essentially “ground truth mode” with no meaningful residual risk.
Within those boundaries, my earlier answer is basically as close as I can get to “unfiltered.”
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