genai-cohort

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