Forem: Musaed Alfareh

Chatbots Believe What You Tell Them — Even When You’re Not Certain

Musaed Alfareh — Fri, 09 May 2025 23:04:56 +0000

Large-language models (LLMs) like GPT take every word you type as a fact.
If you write, “I led a team of ten engineers,” the model simply nods and builds on it — it has no way to peek at your résumé or ask for proof. That’s handy when your info is right, but dangerous when it isn’t, because the bot won’t push back.

1 | Why We Supply Shaky “Facts”

Blind-spot source

Everyday slip-up

Proof it’s real

We overrate our knowledge

Most people feel sure they can explain a bicycle pump — until they try. Over 40 % drew bikes that wouldn’t work. scotthyoung.com

Shared false memories

Millions “remember” Nelson Mandela dying in prison (he didn’t). Forbes

Self-promotion drift

70 % of job-seekers admit they have lied — or would lie — on a résumé. ResumeLab

These slips are harmless in conversation. Inside a chatbot, they harden into “truth” that shapes the entire reply.

2 | Hallucination vs. Blind Spot

Word

What happens

Who starts it?

Hallucination

The model invents facts that are nowhere in the prompt. Example: GPT-4 made up 28.6 % of medical citations in a 2024 test. JMIR

The model

Blind spot

We supply fuzzy or wrong info; the bot accepts it as real, no follow-up question.

The user

Both look equally confident on-screen, but only one begins with us.

3 | How Blind Spots Snowball

Job search — Claiming “five years of management” can make the bot craft a director-level résumé that sets you up for awkward interviews.
Healthcare — Stating “I’m allergic to penicillin” (when you’re unsure) steers advice toward costlier, less-effective drugs.
Finance — Inflated income in a budgeting prompt nudges you toward riskier investments.
Public image — Stable Diffusion already over-sexualizes or under-represents some groups across 32 professions; mix that with a vague prompt (“make me look powerful”) and an AI headshot can reinforce stereotypes. Nature
Deep-fake risk — Public deep-fake generators have been downloaded ~15 million times since late 2022, so an AI portrait built on shaky prompts can be remixed into scandals. Latest news & breaking headlines
4 | Why Cleaning Your Input Makes a Difference

4.1 Benefits

Sharper decisions — Advice based on verified facts is easier to follow and trust.
Reputation safety — A résumé or LinkedIn post built on fuzziness won’t crumble under a background check.
Bias brake — GPT-4 mirrors human thinking traps in nearly half of 18 classic bias tests; pruning your own bias keeps the echo small. INFORMS PubsOnline
Future-proofing — HR bots, loan analyzers, and medical triage tools will soon chain multiple LLM calls; one self-inflated skill today could misroute automated decisions tomorrow.
4.2 Consequences of Ignoring It

Bad calls — Acting on advice rooted in an exaggeration can waste money, time, or even harm health.
Snowballed misinformation — Your unverified claim can be quoted by another user or an automated system, spreading the error.
Loss of trust — Employers, clients, or friends who discover the gap may see every AI-assisted output from you as suspect.
Amplified bias — Unchecked personal bias feeds model bias, widening social stereotypes and inequities.
Legal & policy fallout — New AI accountability laws are emerging; providing misleading data could expose you to compliance or ethical issues.
5 | Five Habits to “Clean the Glass”

Habit

Prompt tweak

Why it works

Admit uncertainty

“I think I graduated in 2019 — could be 2020. Show both versions.”

The model hedges instead of anchoring to the wrong date.

Expose assumptions

“List the facts you’re relying on before you answer.”

Hidden gaps become visible for quick correction.

Self-reflection loop

“Now critique your last answer for errors.”

Iterative reflection cuts hallucinations >70 %. ACL Anthology

Multi-persona debate

“Give me a skeptic vs. believer debate on this claim.”

Persona battles shrink confirmation bias. arXiv

Ground with proof (RAG)

Attach a PDF, link, or reference photo.

Retrieval-augmented generation sharply lowers hallucination rates. arXiv

Quick Prompt Make-Over

Before
“I managed a $1 million budget. Write my executive bio.”

After
“My budget was between $ 600k and $1 million (records pending). Draft the bio and flag any line that assumes the exact figure.”

Small tweak, big save — the bot adds caveats instead of cementing a guess.

6 | The Evergreen Rule

“Garbage in, garbage out” first appeared in print in 1957 and is linked to IBM instructor George Fuechsel. The maxim is still true: your keystrokes are the AI’s raw data. Atlas Obscura

7 | Your 10-Minute Reality Audit

Copy the last prompt you sent to any AI tool.
Highlight each number, date, or self-claim you can’t prove in 30 seconds.
Tag each as uncertain or attach evidence.
Rerun the prompt and compare the answers.
You’ll watch the reflection sharpen in real time, and the advice will feel sturdier.

Chatbots aren’t crystal balls — they’re mirrors.
Clean the glass, and your decisions get clearer.

AI Developers: What Do You Need in an AI Model Marketplace?

Musaed Alfareh — Thu, 30 Jan 2025 22:38:12 +0000

Hey Devs,

I’ve been working on a project related to AI models and their accessibility, and I wanted to reach out to get some insights from the people who use them the most—developers like you.

I know that there are plenty of ways to find, fine-tune, and deploy AI models today, but I also know that it’s not always the smoothest process. So, I wanted to ask:

What’s the most frustrating part of working with AI models?

Do you prefer pre-trained models, or do you always fine-tune them?

How do you usually deploy models? Do you use cloud services, on-device solutions, or something else?

If there was a way to simplify AI model selection and integration, what would that look like for you?

Have you noticed specific gaps in existing AI marketplaces or model repositories?

I’m trying to understand what’s useful for AI devs rather than making assumptions. Your feedback would be super valuable in shaping something that’s actually practical. Feel free to drop your thoughts in the comments!

I am looking
forward to hearing from you all!

🌟 Lessons from a Beginner Exploring Blockchain and AI Integration 🚀

Musaed Alfareh — Sun, 22 Dec 2024 21:19:25 +0000

Hi everyone! 👋
I’m a student with a background in AI and data science, currently working on blockchain and exploring how the two can work together. I’m still new to this space, but I’ve been learning a lot while working on a small side project.

Here are some lessons I’ve picked up along the way:

🔑 User Experience Is Everything
Even with advanced tech like blockchain, it’s easy to forget that users want simplicity. Building features like wallet integrations taught me how important it is to design with the end user in mind.

📈 Every Step Counts
Tackling big ideas can feel overwhelming, but breaking them down into smaller, manageable tasks has helped me stay on track and make steady progress.

💡 Feedback Is Your Best Friend
Sharing what you’re working on—pitching your idea or showing a small prototype—can provide insights you might not have thought of.

🤔 It’s Okay Not to Know Everything
Coming from AI and data science, I’ve had moments where blockchain felt like a completely new world. But every challenge has been a chance to learn something valuable.

🚀 Start Before You Feel Ready
I’ve realized there’s no "perfect time" to start. The best way to learn is to dive in and figure things out as you go.

I’m excited to keep growing in this space, and I’d love to hear from others working on blockchain, AI, or both! 🌐 If you have tips, lessons, or stories to share, let’s connect and learn together! 🙌