u/NoMusician464

Situation: A construction company has an internal project management agent that needs to access weather data for better project briefings.

Question: Describe the technical steps and considerations involved in an agent invoking a tool, passing parameters, and processing the results, including error handling and state management.

• Walk through the process of an agent using a tool to retrieve weather data, from the agent's decision to use the tool to processing the returned information.
• How should an agent handle a scenario where an integrated tool returns an error or an unexpected data format?

Here is my roadmap of topics for AI Engineering Practice. What am I missing. what should be removed, what area do you personally need the most work on?
*** I'll prioritize community challenge questions and explainers for topics with the most requests.

1. Foundations of Artificial Intelligence
2. Principles of Large Language Models
3. Agentic AI Concepts and Architecture
4. Agent Development and Design
5. Multi-Agent Systems (MAS) and Interaction
6. Distributed Reasoning and Coordination
7. Tools and Model Integration for Agent
   8.Agent Orchestration Frameworks
8. Agent Evaluation and Benchmarking
9. Scalablity and Performance Optimization
10. Deployment Strategies for Agentic AI
11. Observability and Monitoring Agentic AI
12. Reliability Guardrails and Safety of Agentic AI
13. Ethical Considerations of Agentic AI
14. Rapid Prototyping of Agentic Solutions
15. User Interface Design for AI Agents
16. Governance and Compliance in Agentic AI
17. Advanced Topics in Agentic AI
    - Advanced Agent Architecture Patterns
    - Autonomous Agent Self Improvement
    - Continual Learning in Agentic Systems
    - Emergent Behavior in Agent Swarms
    - Adversarial Robustness for Agentic AI
    - Federated Learning for Agentic AI
    - Explainable AI for Agents

A Fortune 500 client wants a GenAI assistant for their internal consulting teams. The assistant should answer questions using internal documents, client deliverables, Slack/Teams messages, project notes, and reusable proposal content.

The client is worried about hallucinations, access control, confidential client data leakage, latency, and whether users will trust the answers.

Question:

Design the system architecture for this assistant.

Please cover:

1. Core components
2. Retrieval strategy
3. Access control/security
4. Evaluation strategy
5. Human-in-the-loop or escalation design
6. How you would roll it out from prototype to production

Gemini has advantages for video ingestion, voice, scientific content and has an outstanding medical model.

OpenAI and Anthropic are competing for capabilities in Code Generation
- Anthropic is the most expensive, but highest performing for Code Gen.
- OpenAI provides lower prices with higher efficiency MixtureOfExperts models.

OpenAI and Gemini compete on multimodal models.

OpenAI outperforms the rest on deep research quality.

What are you seeing? Would love to hear about how others are evaluating the differences between Frontier in more objective, data-backed terms.

Gemini has advantages for video ingestion, voice, scientific content and has an outstanding medical model.

OpenAI and Anthropic are competing for capabilities in Code Generation
- Anthropic is the most expensive, but highest performing for Code Gen.
- OpenAI provides lower prices with higher efficiency MixtureOfExperts models.

OpenAI and Gemini compete on multimodal models.

OpenAI outperforms the rest on deep research quality.

What are you seeing? Would love to hear about how others are evaluating the differences between Frontier in more objective, data-backed terms.

I was struggling with getting past the automated ats. I come from a non-traditional experience path, so you could imagine 100s of apps and zero interviews.

(AI Engineering)

I followed this approach to land interviews, hopefully it helps people out there:

1. ⁠Create a LinkedIn profile that showcases your fit for the roles you’re pursuing. I was pursuing applied ai roles, so I framed all of my work history, education, side projects, and posted articles to showcase the persona that recruiters are looking for. GPT or Claude were really helpful in taking the job descriptions I was chasing and recommending improvements to my LinkedIn profile.

2. ⁠Ask gpt to help you create an ‘experience bank’ that lists out all of your skills and experience per role or project. This will be used as a source of truth by gpt queries as you craft additional content and recruiter/hiring manager outreach.

3. ⁠Ask ChatGPT or Claude to provide a spreadsheet of 50 roles that meet your income and desired contribution (ai engineer, design manager…). The spreadsheet should include LinkedIn urls for contacts at that company that are likely hiring managers or recruiters.

4. ⁠Ask gpt to review your LinkedIn profile, experience bank and the target job list, then provide a step by step list of tasks that you should take to land AJ interview. (This won’t be perfect, but it will give you some ideas)

5. ⁠You may be asked to create a portfolio, complete volunteer hours for experience, take a certification course, or begin outreach.

6. ⁠When you’re ready for outreach: fine tune connect messages for your LinkedIn contacts based on your experience, and the person you’re contacting. It needs to be relevant, sound authentically human, and ask for a connection.

7. ⁠Create LinkedIn articles that are relevant to sets of roles in the target spreadsheet. Publish 1 article every 2-3 days and tag 4-5 target contacts from the spreadsheet.

8. ⁠Conduct outreach for 100 - 200 contacts, refine your approach and monitor connection acceptance rates, followup messages, and interview offers.

I found that this approach got me invited to interview for the role and skipped the ats system altogether.

Hope this helps!

I’m working on a learning/practice app for people trying to switch careers or upskill into a new role. The dream is to help people participate in the workforce the way they want instead of keeping skills gated by costly education or experience.

The idea is simple: instead of only reading lessons or watching videos, you can practice applying a skill in realistic workforce scenarios, get feedback, and identify what you still need to work on. Most importantly, prove that you can actually perform.

I’m especially interested in this because AI is changing a lot of roles quickly, and I think career switching will need to become more fluid and less dependent on traditional credentials alone.

I’m not here to sell anything. I’m looking for a small group of people who are actively trying to learn a new skill or move into a different role and would be willing to test it for free and give honest feedback.

A few areas people are trying so far:

• Data structures and algorithms
• Project management
• Product management
• Software development
• SQL
• UX design
• Leadership
• Accounting
• AI / agentic AI certification prep

If you’re currently upskilling or trying to change careers, I’d be curious: what role are you trying to move into, and what part of the learning process feels hardest right now?

Happy to set up free access for anyone who seems like a good fit and is willing to give feedback.

Here are the topics I'm covering for the technical interview. Any recommendations on what I'm missing or what is unnecessary would be great: 

1. Core Python for Data & AI 
2. Fundamentals for NLP 
3. Deep Learning Foundations 
4. Generative AI Model Architecture 
5. Data Ingestion and Knowledge Graphs 
6. Semantic Search and Vector Similarity 
7. Retrieval Augmented Generation 
8. Advanced Prompt Engineering 
9. AI Agents and Tool Utilization 
10. GenAi System Design and Architecture 
11. Evaluating and Benchmarking  
12. Enterprise-grade Ai Governance 
13. Monitoring, Observability, and Telemetry 
14. Deployment and MLOps for GenAI
15. Business Impact and Client Engagement

 

I'm trying to monetize STEM youtube video's with paid content, but creating study guides is becoming time consuming.

1. Has anyone had success selling paid course material packages to subscribers?
2. Is manual creation the best way for quality question banks etc...?
3. How did you manage access and sales? (Currently using shopify for digital downloads, but adding new course material is clunky/hard to navigate).

Here are the topics I'm covering for the technical interview. Any recommendations on what I'm missing or what is unnecessary would be great: 

1. Core Python for Data & AI 
2. Fundamentals for NLP 
3. Deep Learning Foundations 
4. Generative AI Model Architecture 
5. Data Ingestion and Knowledge Graphs 
6. Semantic Search and Vector Similarity 
7. Retrieval Augmented Generation 
8. Advanced Prompt Engineering 
9. AI Agents and Tool Utilization 
10. GenAi System Design and Architecture 
11. Evaluating and Benchmarking  
12. Enterprise-grade Ai Governance 
13. Monitoring, Observability, and Telemetry 
14. Deployment and MLOps for GenAI
15. Business Impact and Client Engagement

 

I’m curious how educational YouTube channels are thinking about monetization beyond ads, sponsors, and one-off course sales.

For channels that teach technical subjects, exam prep, coding, finance, languages, engineering, etc., it feels like there’s often a gap between “the viewer watched the lesson” and “the viewer can actually apply the material.”

Some possible add-ons I’ve seen or thought about:

- Study guides tied to a playlist or course

- Practice questions after each video/module

- Flashcards or spaced repetition

- Diagnostic quizzes to show students what they misunderstood

- Private communities or office hours

- Cohorts / workshops

- Paid templates, notebooks, worksheets, or problem sets

- Certification prep paths

- Progress tracking or completion certificates

For educational creators who have tried this, what has actually worked?

Did viewers pay for practice materials, study guides, or assessments? Or do they mainly value video content and community access?

I’m especially interested in what feels sustainable for creators without creating a ton of extra support burden.

Hi r/tutors, I’m one of the builders behind Flourishly.com, and I’d really value feedback from individual tutors.

We built a learning app designed to support what happens between tutoring sessions, so tutors can spend less time guessing where a student is stuck and more time targeting the right concepts during live sessions.

The basic idea:

A tutor or student can upload or enter a subject, syllabus, homework, notes, PDFs, lecture slides, or video/lecture transcripts. Flourishly then generates:

•	A concept-level knowledge graph

•	Smart flashcards

•	A Socratic assessment chat

•	Mastery tracking across the concept graph

•	Reports showing which concepts the student is forgetting, misunderstanding, or getting frustrated by

The goal is not to replace tutors. It’s the opposite: we want to give tutors a clearer picture of what’s happening between sessions, so they can walk into the next meeting knowing:

“Here are the exact concepts this student needs help with.”

“Here’s what they keep forgetting.”

“Here’s what they can explain but not apply.”

“Here’s where their confidence/frustration is dropping.”

We originally imagined this as a companion tool tutors could assign between sessions, especially for students who need more structured practice but don’t always know what to study next.

A few questions for tutors here:

1.	Would this be useful in your tutoring workflow?

2.	Would you want the tutor to control the content, or would you let students upload their own notes/homework?

3.	What would make you trust or distrust AI-generated flashcards, diagnostics, or mastery reports?

4.	Would parents/students actually value this as part of your tutoring service?

5.	What would you need to see before using something like this with real students?

I’m not here to hard sell. We’re trying to understand whether this solves a real pain point for tutors, especially independent tutors who want better between-session visibility without adding a ton of admin work.

Would love honest feedback even if the answer is “this sounds unnecessary” or “I would only use it if it did X.”