Product, business & communication
Explain how LLMs work and where they fail
Tokens, context, hallucination, RAG vs fine-tuning, cost drivers — well enough to make decisions with engineers.
~8 focused hours·beginner
Tools: ChatGPT/Claude playground, Google AI Studio, tiktoken tokenizer, Hugging Face Inference Playground
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Explain tokens, context windows, and latency/cost trade-offs to a non-technical stakeholder
- Explain why an LLM hallucinates and how grounding/RAG reduces (not eliminates) it
- Compare RAG vs fine-tuning vs plain prompting for a given business problem and justify the choice
- Read a model provider's pricing page and estimate monthly API cost for a proposed feature
- Explain embeddings and vector search well enough for an engineer to trust your product calls
- Spot when a demo answer is confidently wrong and explain why in plain language
- Translate a vague 'add AI to the product' ask into a scoped, buildable capability
Learn — free, link-checked
The few resources that matter
Watch · beginner · 27 min · youtube.com
Transformers, the tech behind LLMs | Deep Learning Chapter 5
Builds real intuition for tokens/attention so you can explain LLM behavior without hand-waving. — 3Blue1Brown
Read · beginner · 30 min · docs.anthropic.com
Prompt Engineering Overview
Official, up-to-date techniques (system prompts, few-shot, chain-of-thought) with before/after examples you can copy. — Anthropic
Course · beginner · 45 min · learn.microsoft.com
Introduction to generative AI and agents
Free, current module covering LLMs and agents with quizzes so you can self-check understanding. — Microsoft Learn
Course · beginner · 60 min · cloudskillsboost.google
Introduction to Generative AI
Vendor-neutral, structured primer on what GenAI is and how it differs from traditional ML — good for a first pass. — Google Cloud Skills Boost
Course · beginner · 180 min · microsoft.github.io
Generative AI for Beginners
21 free lessons with code you run yourself — the fastest path from zero to a working prototype. — Microsoft
Course · beginner · 240 min · deeplearning.ai
Generative AI for Everyone
Andrew Ng's free, non-technical framing of where GenAI creates business value — exactly the PM lens you need. — DeepLearning.AI (Andrew Ng)
Build from · intermediate · 120 min · github.com
Anthropic courses
Anthropic's own hands-on notebooks for tool use, structured extraction, and grading model output quality. — Anthropic
Practice
Explainer deck + cost model for a Hindi/Hinglish WhatsApp support bot
Pick a real Indian business scenario — an NBFC's WhatsApp helpline fielding Hindi and Hinglish loan-repayment questions — and build a one-page, jargon-free explainer of how an LLM-based bot would answer, where it can go wrong, and how RAG grounds it in the NBFC's actual policy documents. Pair it with a simple spreadsheet that estimates monthly token cost at 3 volume tiers (1k/10k/100k conversations) using a real provider's published pricing. Present both in a 10-minute walkthrough recorded as if to a non-technical founder.
Done when
- Explainer avoids all ML jargon (no 'transformer', 'attention', 'logits') while staying technically accurate
- Cost model cites the actual provider pricing page and shows the token-count assumptions used
- At least 2 concrete hallucination failure modes are described with a mitigation for each
- A recorded walkthrough (5-10 min) exists and is linked from the README
Prove it
Evidence a recruiter can check
- Recorded 5-10 minute walkthrough video linked in a public repo README
- Spreadsheet/notebook with the cost model and cited pricing sources
- One-page explainer PDF or slide deck, publicly viewable
- A short LinkedIn or blog post summarizing the RAG-vs-fine-tuning decision and why
Interview
Questions you'll get asked
- Explain to a non-technical VP why our chatbot sometimes makes things up.
- When would you use RAG instead of fine-tuning, and why?
- What is a token, and why does context length matter for both quality and cost?
- Walk me through what happens end to end when a user sends a prompt to an LLM API.
- What's the difference between embeddings-based search and a knowledge graph?
- How would you estimate the monthly API cost for a support-chat feature handling 10,000 conversations/month?
- Give me three distinct ways an LLM-powered feature can fail in production.