10-Day Program Structure
A 10-day intensive program for engineering students who want deep intuition for AI systems, hands-on API projects, and portfolio-ready work for internships and interviews.
10 days · 3 phases
Phase 1: AI Systems Deep Dive
LLMs, Tokens & Transformers
Build strong mental models for how large language models work — tokens, attention, and prediction — without getting lost in math.
Topics
- •Tokenization and embeddings
- •Transformer architecture intuition
- •Training vs inference
- •Model capabilities and limits
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APIs, Embeddings & Vector Basics
Understand the building blocks of modern AI applications — APIs, vector stores, and how data flows through AI systems.
Topics
- •REST APIs for LLM services
- •Embeddings explained practically
- •Vector similarity search
- •Cost and latency considerations
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Prompt Engineering & Tool Use
Master structured prompting, chain-of-thought, and function calling — the skills that separate hobbyists from engineers.
Topics
- •System prompts and few-shot learning
- •Structured outputs (JSON mode)
- •Function calling and tool use
- •Evaluation and iteration
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Phase 2: Building with AI
RAG Architecture
Design and implement Retrieval-Augmented Generation pipelines — the most common pattern in production AI apps.
Topics
- •Chunking and indexing strategies
- •Retrieval quality and reranking
- •Context window management
- •RAG evaluation metrics
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Fine-Tuning vs Prompting
Learn when to prompt, when to fine-tune, and when to use both — with clear decision frameworks.
Topics
- •Fine-tuning use cases
- •LoRA and parameter-efficient methods
- •Data preparation for fine-tuning
- •Build vs buy decisions
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Building with LLM APIs
Hands-on session building a real application using LLM APIs — from design to working prototype.
Topics
- •Project architecture
- •Streaming responses
- •Error handling and retries
- •Rate limits and caching
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Agents & Workflows
Explore multi-step AI agents, workflow orchestration, and when agentic patterns add value vs complexity.
Topics
- •Agent loops and planning
- •Tool orchestration patterns
- •LangChain / LangGraph concepts
- •Debugging agent failures
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Phase 3: Ship & Interview Prep
System Design for AI Apps
Apply system design mental models to AI-powered applications — scalability, reliability, and cost at scale.
Topics
- •Architecture patterns for AI apps
- •Caching and batching strategies
- •Monitoring and observability
- •Common interview system design questions
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Portfolio Project Sprint
Dedicated build day to polish your portfolio project with mentor feedback and code review.
Topics
- •README and documentation
- •Demo video creation
- •GitHub portfolio best practices
- •Code review session
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Demo Day & Interview Prep
Present your project, practice technical interview questions, and get a personalized career roadmap.
Activities
- •Project demo presentation
- •Technical Q&A practice
- •Resume and LinkedIn review
- •Mock interview round
- •Certificate and next steps
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