AI-First Developer | Enablers

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AI-First Developer

Sessions Venue
Online & On Campus Apply Now

Benefits to join Boot Camp

2 Months of Detail Sessions (Face to Face & Online)

Enablers Trainer Support Program (EMS)

Dedicated Student Support on Private Facebook Group

The Upgraded Private Enablers Community

Course Overview

  • The future of software development belongs to developers who know how to work with AI — not just write code from scratch. AI-First Developer is an intensive, end-to-end Programme that takes you from Python fundamentals all the way through to building full-stack applications, AI-powered tools, and automated workflows using the latest AI-assisted development techniques.
  • Across 16 comprehensive modules, you will master Python, understand how large language models actually work, call real AI APIs, build machine learning concepts, design frontend interfaces, and deploy complete applications — with AI as your co-developer at every step. No prior coding experience is needed to begin

Learning Outcomes

  • Build a solid Python foundation
  • Master AI-assisted and vibe coding
  • Call and integrate LLM APIs in real applications
  • Apply data science and machine learning concepts
  • Ship complete, production-ready projects

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Module Breakdown

  • Setting Up the Workspace
    • Installing Python and configuring VS Code with the Python extension
    • Creating and running a first script from the integrated terminal
    • Reading interpreter output and basic tracebacks
  • How Code Runs
    • How the Python interpreter reads and executes a program line by line
    • The role of indentation, statements, and comments
    • Why Python is readable and how this helps when reviewing AI-generated code
  • Variables, Data Types & Operators
    • Integers, floats, strings, and booleans
    • Declaring variables and basic type conversion
    • Arithmetic, comparison, and logical operators
    • Reading input and printing output
  • Making Decisions
    • if /elif /else branching and truthiness
    • Combining conditions with logical operators
    • Nesting and reading conditional logic
  • Loops
    • for loops and iterating withrange
    • while loops and loop conditions
    • break andcontinue to control a loop
  • Collections
    • Lists and basic list operations
    • Tuples and when they are used
    • Dictionaries as key–value stores
    • Working with strings and common string methods
  • Writing Functions
    • Defining functions and calling them
    • Parameters, arguments, and default values
    • Return values and how data flows out of a function
    • Variable scope: local versus global
  • Organizing Code
    • Splitting a program into functions for clarity
    • Modules and imports — reusing code across files
    • A first look at the standard library (math,random, datetime)
  • Handling Errors Gracefully
    • What an exception is and why programs raise them
    • Basictry / except to handle errors
    • Reading an error message to understand what went wrong
  • Reading and Understanding Code
    • Following the flow of an existing Python script
    • Recognizing the building blocks learned so far in real code
    • Knowing enough to direct, review, and correct generated code
  • Libraries & Environments at a Glance
    • Installing packages with pip and what a library provides
    • The idea of a virtual environment and why projects stay isolated
    • Where code lives in a project and how files connect
  • Coding with AI Assistants
    • Using GitHub Copilot for inline suggestions in VS Code
    • Driving changes with Claude Code from the terminal
    • The course philosophy: you understand how the language works, AI writes the code, and you direct and review it
  • The Model's View of Text
    • Tokens and tokenization — why characters, words, and tokens differ
    • Context windows, input/output limits, and what gets truncated
    • The autoregressive next-token loop in plain terms
  • Sampling and Determinism
    • Temperature, top-p, and their effect on output variability
    • When to favor deterministic vs. exploratory generation
    • System, user, and assistant roles in a chat transcript
  • Capabilities and Limits
    • Knowledge cutoffs, hallucination, and where models fail
    • Comparing responses across ChatGPT, Claude, and Gemini
    • Reading model documentation to choose the right model
  • Anatomy of Good Context
    • The four building blocks: role, task, examples, output format
    • Few-shot examples and how they steer behavior
    • Constraining responses to JSON and other parseable formats
  • Reusable Prompt Patterns
    • Zero-shot, few-shot, chain-of-thought, and decomposition patterns
    • Templating prompts with variable slots for reuse
    • Iterating systematically and measuring quality changes
  • Versioning Context in Git
    • Storing prompts as files alongside code
    • Tracking prompt revisions with commits and diffs
    • A/B comparing prompt versions across multiple models
  • Mapping the Landscape
    • Foundation models, hosted APIs, and open-source weights
    • Proprietary vs. open models and the trade-offs of each
    • How providers differ on context size, speed, and pricing
  • Getting the Most from Chat Interfaces
    • Advanced use of ChatGPT, Claude, and Gemini for real tasks
    • Custom instructions, projects, and conversation memory
    • File uploads, document analysis, and long-context workflows
  • Practical Model Selection
    • Matching task type to model strengths
    • Cost, latency, and quality as selection criteria
    • Building a personal benchmark across the three tools
  • Beyond Text
    • Image, audio, and document inputs to multimodal models
    • Vision tasks: description, extraction, and reasoning over images
    • Combining modalities in a single prompt
  • Image Generation
    • Generating images and iterating on prompts for control
    • Style, composition, and reference-guided generation
    • Practical limits, artifacts, and content constraints
  • Comparative Workflows
    • Running the same multimodal task across ChatGPT, Claude, and Gemini
    • Evaluating output fidelity and consistency
    • Choosing a tool per use case based on results
  • First API Calls
    • Authentication, API keys, and storing secrets safely
    • Making completion requests to the OpenAI, Claude, and Gemini APIs
    • Inspecting responses, token usage, and finish reasons
  • Structured Outputs
    • Defining schemas with Pydantic models
    • Forcing valid JSON output and validating it on return
    • Handling parsing failures and retries
  • Streaming and Responsiveness
    • Consuming streamed token responses
    • Rendering partial output for better UX
    • Cancelling and timing out long generations
  • Function Calling and Tools
    • Exposing functions to a model as callable tools
    • Parsing tool-call requests and returning results
    • Building a simple tool-using loop
  • Composing Multi-Step Workflows
    • Prompt chains: feeding one model output into the next
    • Orchestrating chains, memory, and tools with LangChain
    • Structuring agents that decide which step to run
  • Operating in Production
    • Rate limits, backoff, and retry strategies
    • Tracking and controlling token cost across calls
    • Logging, caching, and error handling for reliability
  • Loading and Inspecting Data
    • Series and DataFrames; reading CSV, JSON, and Excel
    • First-look inspection: shape, types, head/tail, and summary statistics
    • Indexing and selection with `loc` / `iloc`, filtering, and sorting
  • Data Cleaning
    • Handling missing values, duplicates, and inconsistent entries
    • Type coercion, parsing dates, and normalizing text
    • Detecting and treating outliers
  • Transformation and Feature Work
    • `groupby`, aggregation, joins, and reshaping (pivot/melt)
    • Deriving new columns, binning, and encoding categories
    • Vectorized operations with NumPy under the hood
  • Exploratory Data Analysis & Visualization
    • Distributions, correlations, and descriptive statistics
    • Plotting with Matplotlib and Seaborn: histograms, scatter, box, and line charts
    • Telling a story from data and working interactively in Jupyter
  • The Modeling Workflow
    • Framing classification and regression problems
    • Train/test splits, validation sets, and cross-validation
    • Fitting and predicting with scikit-learn estimators
  • Core Algorithms
    • Linear and logistic regression
    • Decision trees and ensemble methods
    • Feature scaling, encoding, and pipelines
  • Honest Evaluation
    • Accuracy, precision, recall, and F1 — and when each matters
    • Confusion matrices and threshold selection
    • Diagnosing overfitting, underfitting, and data leakage
  • Neural Network Intuition
    • Neurons, weights, biases, and activation functions
    • The forward pass, loss functions, and what training optimizes
    • Gradient descent and backpropagation, conceptually
    • A tiny PyTorch model: the training loop in practice
  • Embeddings as the Central Idea
    • Representing words and items as vectors
    • Similarity, distance, and what embedding space encodes
    • Using embeddings for search, clustering, and retrieval
  • Transformers & Pretrained Models
    • Self-attention and why it replaced recurrence
    • The transformer block and modern architectures
    • Loading and running pretrained models from Hugging Face
    • Fine-tuning vs. prompting vs. retrieval
  • Design Language
    • Visual hierarchy, spacing, alignment, and contrast
    • Color systems, typography, and consistency
    • Design systems, components, and design tokens
  • UI/UX Concepts
    • User flows, information architecture, and navigation
    • Usability principles and accessibility basics
    • Responsive design and common interface patterns
  • Frontend Basics
    • How a web page is structured: HTML, CSS, and components
    • The role of layout, state, and interactivity at a high level
    • Reading and directing AI-generated frontend code with intent
  • Building the Frontend
    • Generating interfaces with Lovable, v0, and Cursor
    • Turning a design intent into working, responsive UI
    • Iterating on layout, components, and styling through AI
  • Backend
    • Building APIs with FastAPI and connecting them to the UI
    • Modeling data, authentication, and core endpoints
  • Databases
    • Relational databases: Postgres, Supabase, and SQLite for structured app data
    • Document and NoSQL storage with MongoDB for flexible, schema-light data
    • Vector databases like Pinecone for embeddings, semantic search, and RAG
    • Choosing the right database for the application's needs
  • Deployment
    • Connecting a GitHub repo for serverless deployment on Vercel
    • Environment variables, secrets, and production configuration
  • No-Code AI Agents
    • Building automation workflows in n8n
    • Connecting services and triggers with Make.com
    • Wiring LLM calls into automated, event-driven agents
  • Deployment
    • Containerizing services with Docker where needed
    • Connecting a GitHub repo for serverless deployment on Vercel
    • Environment variables, secrets, and production configuration
  • Capstone
    • Architecting and building a full-stack AI application end to end
    • Producing a live URL, a complete GitHub repository, and an architecture diagram
    • Demoing the application and walking through the build

Frequently Asked questions (FAQs)

AI-First Developer

Learn to build and scale applications using AI workflows.

The AI-First Developer course is designed to help students learn programming, AI tools, and modern software development with an AI-first approach. Students learn Python, AI-assisted coding, LLMs, APIs, data science, machine learning, frontend basics, full-stack development, automation, and deployment.
This course is suitable for students, beginners, aspiring developers, freelancers, software learners, data science beginners, and anyone who wants to build modern AI-powered applications.
No. The course starts from Python basics, including variables, data types, loops, functions, and error handling. It is beginnerfriendly and gradually moves toward advanced AI and full-stack development topics.
Students will learn Python fundamentals, including syntax, variables, operators, conditions, loops, lists, dictionaries, functions, modules, imports, and basic error handling.
Yes. Students will learn how to use AI coding assistants such as GitHub Copilot, Claude Code, Cursor, Lovable, and v0 to generate, review, improve, and direct code.
The course teaches both. Students learn enough programming fundamentals to understand how code works, while also learning how to use AI tools to write, modify, and review code more efficiently.
An AI-first developer uses AI tools as part of the development workflow. Instead of writing every line manually, the student learns how to describe requirements clearly, guide AI tools, review generated code, test outputs, and improve the final application.
Yes. The course covers how language models work and compares tools such as ChatGPT, Claude, and Gemini for development, reasoning, document analysis, coding support, and practical tasks.
Yes. Students will learn tokens, tokenization, context windows, temperature, top-p, system/user/assistant roles, hallucinations, knowledge cutoffs, and model limitations.
Context engineering means giving AI tools the right instructions, examples, role, task, and output format so that they produce more accurate and useful results. Students will learn reusable prompt patterns and structured prompting.
Yes. Students will learn prompt patterns such as zero-shot, few-shot, decomposition, structured outputs, JSON formatting, and reusable prompt templates.
Yes. The course introduces versioning context and prompts in Git, including storing prompts as files, tracking changes, comparing versions, and improving prompt quality over time.
Yes. Students will learn how AI models work with text, images, audio, and documents. They will also learn image generation, prompt iteration, style control, composition, and practical limits.
Yes. The course teaches how to call OpenAI, Claude, and Gemini APIs, use API keys safely, inspect responses, track token usage, handle structured outputs, and manage streaming responses.
Yes. Students will learn how to expose functions to models, parse tool-call requests, build simple tool-using loops, create prompt chains, and understand orchestration with tools such as LangChain.
Yes. Students will learn important production concepts such as rate limits, retries, backoff strategies, token cost tracking, logging, caching, and error handling.
Yes. Students will learn how to load and inspect data from CSV, JSON, and Excel files using pandas, clean data, remove duplicates, handle missing values, transform data, and create visualizations.
Yes. Students will learn basic exploratory data analysis and visualization using tools such as Matplotlib and Seaborn, including charts like histograms, scatter plots, box plots, and line charts.
Yes. Students will learn machine learning concepts such as classification, regression, train/test splits, validation, crossvalidation, linear regression, logistic regression, decision trees, ensemble methods, and model evaluation.
Yes. The course introduces deep learning concepts such as neurons, weights, biases, activation functions, loss functions, gradient descent, backpropagation, embeddings, transformers, and pretrained models.
Yes. The course includes a first look at a small PyTorch model and introduces pretrained models from Hugging Face.
Yes. Students will learn the basics of frontend development, including HTML, CSS, components, layout, state, interactivity, responsive design, UI/UX concepts, accessibility, and design systems.
Yes. The course includes full-stack vibe coding, where students learn how to generate interfaces, build backend APIs with FastAPI, connect frontend and backend, use databases, and deploy applications.
The course introduces Postgres, Supabase, SQLite, MongoDB, and vector databases such as Pinecone for different application needs.
Yes. Students will learn deployment concepts such as GitHub repositories, Vercel deployment, environment variables, secrets, production configuration, and Docker where needed.
Yes. The final part of the course introduces no-code AI agents and automation workflows using tools like n8n and Make.com.
The capstone project requires students to architect and build a full-stack AI application end to end. The final deliverables may include a live URL, GitHub repository, architecture diagram, and project demo.
Yes. The course teaches practical skills in Python, AI coding, APIs, data handling, automation, frontend basics, backend development, databases, deployment, and AI application development, which are highly useful for jobs, internships, freelancing, and startup projects.
Yes. A laptop is strongly recommended because students will install tools, write code, use AI coding assistants, work with APIs, build applications, and deploy projects.
Some tools may have free versions, while advanced features, APIs, or credits may require paid access. The Admission Office should confirm final tool requirements before each batch.
By the end of the course, students should be able to understand programming basics, use AI tools for development, work with LLM APIs, analyze data, understand AI/ML concepts, build full-stack AI applications, and present a final working project.
Students can apply by contacting the Admission Office, filling out the registration form, submitting required details, and completing the admission process according to the institute’s policy.