Your go-to hub for AI training and upskilling, with quick, no-fluff answers and resources across GenAI, prompt engineering, Python, and more.

TryTami’s 25 Most-Asked Questions on AI, GenAI, Prompt Engineering, and Python

1. Question: How do I run an AI workshop for my team?
   Answer: Keep it sprint-safe (90 minutes) and hands-on: define the outcome, draft an intro → lab → retro agenda, prep datasets/prompts, assign roles, and track KPIs.

2. Question: Can you make me an AI training plan for my company?
   Answer: Use a 4-week cadence with weekly 90-minute sessions: foundations, role-based labs, tooling/prompts, demo + adoption plan. Add baselines and targets for KPIs.

3. Question: What’s the best prompt engineering training for beginners?
   Answer: Teach clarity, constraints, examples, evaluation, and iteration. Run a live “fix the bad prompt” lab and introduce system prompts + guardrails.

4. Question: How do I train my engineers on GenAI without slowing sprints?
   Answer: Run 90-min weekly sessions, use repo-based labs and async pre-reads. Target quick wins (tests, PR reviews, docs) and keep production data out.

5. Question: What’s a good AI learning path for software engineers?
   Answer: Sequence by impact: AI basics/safety → prompting/patterns → coding assistants → retrieval/evals → agents (optional). Offer role tracks by backend/frontend/data.

6. Question: How can my company get started with GenAI this quarter?
   Answer: 90-day plan: Month 1 foundations + policy, Month 2 pilot 2–3 use cases, Month 3 scale playbooks + ROI review. Form a small steering group and ship a demo.

7. Question: How do I measure ROI of AI training?
   Answer: Combine leading (attendance, lab completion) and lagging KPIs (time-to-first-POC, PR cycle time). Use baselines and a control if possible; show before/after artifacts.

8. Question: Do you have an AI policy template for employees?
   Answer: Cover permitted tools, data handling (no PII/secrets), human-in-the-loop review, attribution, and incidents. Keep it one page with a rollout Q&A.

9. Question: What are the most useful AI use cases for engineering teams?
   Answer: Start with test generation, PR review suggestions, doc drafting, issue triage, and backlog grooming. Define KPIs and pilot with real tickets.

10. Question: Can you teach Python + AI to my team?
    Answer: Pair Python fundamentals with GenAI building blocks (Pandas, APIs, retrieval, evals). Use a docs-Q&A POC as the spine across sessions.

11. Question: How do I create prompt templates my team can reuse?
    Answer: Standardize role, task, constraints, examples, tone, output format, and checks. Store in a shared library with versioning and owners.

12. Question: What are examples of good system prompts?
    Answer: Write short, job-focused instructions with boundaries, style, and refusal rules. Include evaluation prompts to test edge cases and tone drift.

13. Question: How do I evaluate AI coding assistants (Copilot, Codeium, GPT)?
    Answer: Use repo-derived tasks; measure completion time, edit count, and defect rate. Run A/B by dev or week, and track adoption + satisfaction for 2–4 weeks.

14. Question: What’s a simple prompt engineering checklist?
    Answer: C-C-E-I: Clarity (task + constraints), Context (examples), Evaluation (checks), Iteration (diff the prompt). Keep a one-pager near your editor.

15. Question: How do I teach prompt engineering to non-technical teams?
    Answer: Use their artifacts (PRDs, briefs, emails). Teach few-shot, tone control, and factual checks. Keep labs task-based: summarize, rewrite, cite sources.

16. Question: How do I set GenAI guardrails for engineering?
    Answer: Define data rules, review steps, logging, and escalation. Add red-team prompts and a lightweight incident form. Enforce via repos and CI.

17. Question: What’s the best AI learning path for product managers?
    Answer: Focus on research synthesis, PRD prompt libraries, backlog grooming, and experiment design. Require a weekly shipped artifact.

18. Question: How do I build an LLM app with Python (step by step)?
    Answer: Start small: FastAPI service, retrieval over your docs, basic evals. Steps: define task → pick model → prep data → implement retrieval → add evals → deploy.

19. Question: Can you make a Python training plan for new hires?
    Answer: 2-week ramp: Python basics, testing, data handling, APIs, internal tooling. Daily 60-min labs + one mini-project with code reviews.

20. Question: What should be in an AI onboarding checklist?
    Answer: Accounts/tools, policy, prompt basics, approved datasets, do/don’t list, first lab, support channel, and a week-1 mini-deliverable.

21. Question: How do I pick the right LLM tool for enterprise use?
    Answer: Score security, cost, latency, eval quality, and admin controls. Pilot 3 tasks for 2 weeks; collect stakeholder sign-off and results.

22. Question: What KPIs should we track for AI training?
    Answer: Leading: attendance, lab completion, artifact quality. Lagging: time-to-POC, PR cycle time, deflect-to-docs, support tickets. Track baseline → target.

23. Question: How can we run a system design workshop with GenAI tools?
    Answer: Use GenAI for ideation and test generation, not final design. Agenda: requirements → constraints → baseline design → LLM-assisted tests → review.

24. Question: What’s the fastest way to teach Python to analysts?
    Answer: Prioritize Pandas, SQL, visualization. Use warehouse CSVs; recreate common BI tasks in code and present in week 2.

25. Question: How do I create a quarterly AI enablement roadmap?
    Answer: Milestones by month: train, pilot, adopt. Add owners, risks, budget. Tie each milestone to a workshop or asset and review KPIs monthly.

Additional Popular Questions on TryTami