Google AI Studio
Build
Test prompts and model inputs before turning a feasibility experiment into code.
All capabilities · 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.
Explore 4 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
AI Search Optimization Specialist87%AI Quality Assurance / GenAI Test Engineer79%AI Security Engineer79%AI Product Manager71%AI Solutions / Pre-sales Engineer67%AI Technical Trainer / Instructor61%AI Support Engineer60%AI Content Creator58%AI Governance & Compliance Analyst32%AI-enabled Data Analyst22%AI Data Annotator / Labeling QA15%
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
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
4 tools to explore
OpenAI API
Build · Test
Connect model calls, tool use and structured responses to your own application.
Claude API
Build · Test
Build model-backed features with messages, tool use and responses you can evaluate.
tiktoken
Build
Count tokens and compare how prompt changes affect the input budget.
Practice
Explainer deck and cost model for a Hindi/Hinglish support bot
Pick a real Indian scenario — a lender's helpline fielding Hindi and Hinglish loan-repayment questions — and write a one-page, jargon-free explainer of how an LLM-based bot would answer it, where it goes wrong, and how grounding it in the lender's published policy documents changes the answer. Pair that with a spreadsheet estimating monthly token cost at three volume tiers (1k / 10k / 100k conversations) using a provider's actual published per-token prices. Present both in a 10-minute walkthrough recorded as if to a non-technical founder.
Start from
Milestones
- Tokenise 20 real Hindi/Hinglish questions and measure what one conversation actually costs · ~1.5h
- Build the three-tier cost spreadsheet from the published per-token prices · ~1.5h
- Write the one-page explainer with two named hallucination failure modes · ~1.5h
- Record the 10-minute founder walkthrough · ~1.5h
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
- A three-tier cost spreadsheet showing the token-count assumptions per conversation and linking the pricing page every number came from
- A one-page explainer that survives a jargon check — no 'transformer', 'attention' or 'logits' — and still gets grounding right
- Two hallucination failure modes you actually triggered in the playground, each with the mitigation you would ship
- A 5-10 minute recorded walkthrough pitched at someone who has never used an LLM API
Signal it
Built a token-level cost model for a Hindi/Hinglish support bot across three volume tiers and an explainer that took a non-technical audience to a build/no-build decision without ML jargon.
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.