AI Launchpad

Build AI Apps That Get You Hired

Go beyond ChatGPT — learn how to build and deploy your own AI systems. From fine-tuning LLMs to creating AI chatbots and RAG pipelines, you’ll get hands-on experience building real solutions that impress recruiters and startups alike.

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What You Will Build

Deploy LLMs using frameworks like vLLM, TGI, or Ollama

Create Retrieval-Augmented Generation (RAG) apps

Build AI agents that connect to APIs, databases, and tools

Fine-tune models for specific business use cases

Master Prompt engineering to accelerate problem solving

💡 Each project mirrors what real AI startups are shipping today.

Real Results

“I built my first AI chatbot project here — now I work as an AI Engineer.”

— Video testimonials from our alumni who went from zero to offer letter.

Curriculum

Vibe Coding with VS Code

Learn “vibe coding” workflow in VS Code: AI-assisted planning → code generation → refactor → validate → ship
Use Copilot / ChatGPT-style assistants effectively: prompts for code, debugging, tests, and documentation
Build a mini project end-to-end in VS Code with best practices: git, linting, testing, and clean commits

Prompt Engineering for Business and Data Analytics

Write prompts to generate business insights: KPI definitions, metric trees, trend analysis, executive summaries
Use AI to transform data outputs into decisions: dashboards explanation, root-cause analysis, recommendations, risk flags
Apply validation prompts to prevent wrong insights: assumption checks, data-quality questions, and confidence scoring

Prompt Engineering for Project Management

Use AI to draft and refine project artifacts: project charter, scope, milestones, RACI, risk register, status reports
Create prompts for planning and execution: breakdown tasks, estimate timelines, identify dependencies, manage changes
Build stakeholder communication workflows: meeting agendas, minutes, action items, escalation notes, update emails

Prompt Engineering for QA

Generate test strategy + test plans + test cases (positive/negative/boundary/edge) using structured prompts
Use AI for bug lifecycle support: writing clear bug reports, reproductions, severity assessment, and triage notes
Improve coverage and reduce hallucination: critique prompts, missing-case detection, and requirement-to-test traceability