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JUSTANOTHERPM
985 members • Free
6 contributions to JUSTANOTHERPM
0 likes • 16d
Product: 1. Real job it solves:Helps users get quick, reliable answers, generate content, or explore ideas without starting from scratch. 2. How the system stays grounded:Trained on vetted data and instructions; relies on prompt constraints, safety filters, and moderation to limit hallucinations. 3. Context the model receives:User input (prompts) plus system instructions that define tone, behavior, and scope of responses. 4. Failure modes:Can generate incorrect, biased, or misleading answers; handled via disclaimers, user feedback, and moderation tools. 5. Trade-offs:Prioritizes speed and accessibility over perfect accuracy; balances flexibility with safety constraints. 6. Role of the UI:Provides clear input/output interface, feedback mechanisms, and warnings to build trust and allow user control.
0 likes • 16d
@Phil L Nice. This really shows how ChatGPT balances usefulness with user control and transparency
Dec '25 •Â
Week 1, Activity 2: Personal Inventory
Submit your problem mapping here. 👇 How to Submit 1. Fill out the template from the essay 2. Post your response in the comments below 3. Read at least 2 other people's ideas and leave thoughtful feedback. Let's think this through. 👇
0 likes • 24d
@Phil L Yes, data availability and quality are the biggest hurdles in healthcare, especially in public hospitals. Even with imperfect or partial historical logs, the system can start providing early warnings, but it won’t be perfect at first. The plan would be to: 1. Start with the data that exists (dispensing logs, patient counts, delivery records). 2. Build a predictive model that improves over time as more data is collected and validated.
1 like • 24d
@Jerel Lee Thanks for these thoughts — they’re really helpful and highlight important considerations around data quality, logistics, and expectations. I agree this would require a phased approach and learning from adjacent industries.
Jun '24 •Â
Welcome Aboard - Start Here - Introduce Yourself
Hey there, And a warm welcome to our vibrant community. This is where you start the journey towards making your dreams a reality. This community is not just about product management. Instead, it is about sharing your aspirations, your ambitions, your goals. And then learning the things that will help you achieve the same goals. And the best way to give back to those who helped you along the way is to pass it forward. Help others who are in similar situations as you were by guiding them and sharing the lessons that you learned in your journey So without further ado, let's do this. Let's do it together. Let's meet our professional goals and help others meet theirs. A short intro to this community: You will find three major sections: Community: where you can post and read all the posts on all topics (or choose to filter the ones that are of most interest) Classroom: this is where you will find all the courses and challenges. You will automatically have access to all the FREE resources and the paid courses that you've already bought. Events: this is where you can find a calendar of all the upcoming (and past events) you can RSVP, get access to sign up links and recordings. With that said, enough about the community, let's know you a little bit more. Tell us: - Where you’re from - What you do - What you’re looking to learn or achieve here - A fun fact about yourself Excited to grow and learn with you!
2 likes • Dec '25
Hola everyone, Everyone calls me Peculiar (my real name though). I am a Product Manager based in Lagos, Nigeria, and have worked as a founding pm for 5 years, in a general capacity. I now wish to specialize, become a senior pm, and increase my earning power, hence my signing up for this course. My product experience spans internal tools, a payroll solution, a food delivery app, and most recently, an aquaponics system. Fun fact: My favorite Indian movie is Jamtara—the storyline is pretty relatable.
0 likes • 24d
Thank you Sid!! @Sid Arora
Jan 5 •Â
Week 3 Activity: Does it really need AI
For the idea that you thought of in the Week 2 activity, share the following: Deliverable #1: Share the scores on each dimension and share a short description of why you rated it like that. Deliverable #2: Share the total score (Total Score = add all three) Deliverable #3 : What does your score tell you about your idea?
0 likes • Jan 9
AI Fit Scorecard – Medicine Stock-Out Prediction - Data Readiness: 4/5Hospitals have historical dispensing, inventory, and patient data, though with some gaps. - Output Type: 4/5The output is probabilistic forecasting, not a single correct answer, due to fluctuating demand. - Error Tolerance: 3/5 Errors matter, but the system is advisory with human oversight. Total: 11/15 → I think it's a Strong AI fit.
Jan 1 •Â
Week 2 Activity 1: What tech stack does your product need
Submit your answer here. Keep it simple. Just explain in simple English. Be sure to call out "why" you think you need or don't need a specific aspect in your product. Let's go 👇
4 likes • Jan 9
The Problem: Hospitals can track current medicine stock using tools like QuickBooks, but lack reliable foresight to predict when fluctuating demand will cause critical drugs to run out, resulting in preventable shortages and treatment delays. My understanding of what the problem needs, simply: An AI tool that can predict future consumption rate, estimated stock-out date, and the risk level (E.g, if stress headache drugs run out, there's less risk compared to diabetic drugs running out). Machine learning forecasting tool, I guess. Why not LLM: No natural language reasoning or understanding task is required. Why not RAG: We are not generating new answers where retrieving current relevant info is needed. All the data here is structured numeric data (dispensing logs, patient counts, stock levels, delivery dates) Why not embeddings: We are not doing any search, clustering, or similarity matching. Why not Gen AI: There's no conversation reasoning, or creation of new content needed. Optionally, as a nice to have. RAG and GEN AI, can be used respectively for: textual reasoning relevant for numeric, time-series forecasting. And generating plain-language explanations for alerts. Which could be an overkill.
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@peculiar-ediomo-abasi-5738
Curious, growth-oriented, and intentional about becoming better every day. Here to learn deeply, contribute thoughtfully, and apply what I learn wi..
Active 6d ago
Joined Dec 28, 2025
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