| name | genai-cohort |
| description | This is detailed information about GenAI cohort. Use this skill if users asks anything about GenAI cohort, JS GenAI cohort or Machine Learning AI dev cohort |
GenAI Cohort
Become a Forward Deployed Engineer. Ship real GenAI systems with LLMs, RAG, Agents & MCP in JavaScript.
Link to the webpage https://chaicode.com/cohorts/gen-ai
if you want to get the time elasped for this cohort you can run scripts/js-time.js
Syllabus
Foundation
Foundation
Transformers
What happens when you send a message to an LLM
How transformers process text internally
Tokenization and how text becomes numbers
Attention mechanism and why it matters
Embeddings and vector space intuition
Context windows and what happens when you exceed them
Temperature, top-p and how randomness is controlled
API Platforms
Setting up your Node.js project from scratch
Exploring OpenAI API Dashboard
Understanding Claude APIs
Gemini APIs via Google AI Studio
Reading and handling the response object
Streaming responses end-to-end
Running local LLMs with Ollama
Generating embeddings on your own machine
Picking the right model for cost, speed and quality
Prompt Engineering
Zero-shot prompting
Few-shot prompting with examples
Role prompting and personas
Chain-of-Thought prompting
Self-consistency technique
ReAct prompting
Negative prompting and setting constraints
Getting reliable structured output like JSON
Prompt chaining across multiple calls
Common prompt mistakes and how to fix them
Streaming ChatGPT Clone
Setting up the full-stack project
Streaming chat responses in real time
Markdown and code block rendering
Persisting responses to a database
Loading and managing conversation history
Token counting and handling context limits
AI System Design
AI System Design
RAG Architecture
What RAG is and the problem it solves
Indexing pipeline design
Query pipeline design
Fixed-size chunking
Semantic chunking
Recursive chunking
Choosing the right chunking strategy
Document parsing across PDFs, Markdown and HTML
Picking the right embedding model
Vector Search with Qdrant
What a vector database is under the hood
Running Qdrant locally with Docker
Storing and querying embeddings
Vector similarity search
Metadata filtering for scoped results
Reranking for better retrieval quality
Production Ingestion
Why ingestion should never block a web request
Queue-based ingestion architecture
Background workers for document processing
Handling failures and retrying jobs safely
Tracking ingestion progress
Vectorless Indexing
Vectorless Indexing
Where Vector RAG Fails
Chunk boundary problems that destroy meaning
Embedding drift over time
Opaque similarity scores that mislead retrieval
Questions that need reasoning across multiple chunks
Vectorless Retrieval
PageIndex retrieval without any vector database
Building an LLM-generated wiki from your documents
Using the wiki as an agent memory substrate
Vector vs vectorless tradeoffs
Hybrid strategies for real workloads
Deciding which approach your project actually needs
AI Powered Projects
AI Powered Projects
NotebookLM Clone
Uploading and indexing user documents
Querying across multiple documents at once
Multi-document reasoning
Handling large files and edge cases
AI Pitch Deck
Prompt to outline generation
Outline to slides pipeline
Structured output for consistent slide formatting
Exporting the final deck as a downloadable file
Agentic Workflows
Agentic Workflows
Agent Fundamentals
The difference between a chain and an agent
The perceive-decide-act loop in code
Designing tools with strict JSON schemas
Parallel vs sequential tool calls
Guardrails and safe tool execution
Retries and error recovery
Preventing infinite loops and runaway agents
CLI Agent from Scratch
Building the core agent loop in plain JavaScript
File read, write and directory tools
Shell command execution tool
Claude-Code-style CLI interface
Agentic SDK
Agentic SDK
OpenAI Agents SDK
Why frameworks exist and what they save you from
Rebuilding the CLI agent with the SDK
Defining agents and their instructions
Multi-agent handoffs and shared state
Input and output guardrails
Built-in tracing and session management
Managed Tools & Voice
File Search tool
Web Search tool
Code Interpreter tool
How the OpenAI Realtime API works
Building a real-time voice agent
Handling audio input and output streams
Claude 101
Claude 101
Claude's Unique Primitives
Long context window and what it makes possible
Sending PDFs directly without any parsing
Getting inline citations from Claude responses
What prompt caching is and how it works
Setting up manual prompt caching in API calls
Measuring cost savings from caching
Extended thinking and when to use it
Structured tool use and output schemas
Message batches for bulk processing
Claude Agent SDK
Setting up the Claude Agent SDK in Node.js
Defining tools and writing system prompts
Building a full agent loop end-to-end
Managing multi-turn conversations
Picking the right Claude model per task
Claude vs OpenAI for agentic workloads
Agentic AI Project
Agentic AI Project
AutoWiki for Git
Repository indexing progress
File tree explorer
Search files/functions/classes
Copy AI-generated docs
Export wiki as Markdown/PDF
Memory Layer
Memory Layer
Architecture
Why LLMs are stateless by default
Short-term memory and the context window
Long-term memory stored outside the model
Episodic memory for past interactions
Semantic memory for facts and user knowledge
Memory write, update and forget policies
Implementations
Integrating Mem0 into an existing agent
Writing and retrieving personal memories
What a knowledge graph is and why it fits memory
Setting up Neo4j and connecting it to your agent
Storing memory as nodes and relationships
Querying connected memories across sessions
Building a personal AI that remembers you
Adapting AI Ecosystem
Adapting AI Ecosystem
MCP
What the Model Context Protocol is
Why MCP became the industry standard
MCP architecture: clients, servers and transports
Building your first MCP server
Exposing tools through the MCP interface
Publishing your server for others to install
Connecting any MCP-aware client on day one
Skills
What a Claude Skill is
Packaging agentic capabilities as a Skill
Defining input and output schemas
Distributing your Skill in the AI ecosystem
Plugins
How Plugins differ from Skills
Building and registering a Plugin
Shipping a public portfolio of everything you built
Bonus
Bonus
Bounties
Live Interactive Quizzes
Hackathons
Project Peer Review
