vikasthakurr/ai-for-devs-batch-1

★ 13PythonAudience · developerComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((repo))
    AI Tool Basics
      Tool landscape survey
      Prompt engineering
      Code reliability tips
    Spec Driven Dev
      Structured specs
      AI code workflows
    RAG Systems
      Codebase retrieval
      Document search
    AI Agents
      API calling
      Database queries
      Task chaining
    Capstone Project
      Text to SQL
      Full stack app
      CI CD deploy
    Setup
      Per session notes
      Demo code
      Environment guide

mindmap root((repo)) AI Tool Basics Tool landscape survey Prompt engineering Code reliability tips Spec Driven Dev Structured specs AI code workflows RAG Systems Codebase retrieval Document search AI Agents API calling Database queries Task chaining Capstone Project Text to SQL Full stack app CI CD deploy Setup Per session notes Demo code Environment guide

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Things people build with this

USE CASE 1

Learn to use AI tools in your daily coding workflow without needing a machine learning background.

USE CASE 2

Build a text-to-SQL app where users ask plain-English questions and get database queries back.

USE CASE 3

Understand how to set up RAG so an AI can search your own codebase or documents before answering.

USE CASE 4

Practice building AI agents that can call APIs, query databases, and chain multiple tasks together.

Tech stack

PythonPostgreSQLRAGCI/CDSQLJavaScript

Getting it running

Difficulty · moderate Time to first run · 30min

Requires basic Python or JavaScript comfort. Each session folder has its own setup handbook and demo code. Configure your environment before attending, the course expects live session participation.

No license is mentioned in the explanation.

In plain English

This repository holds the course materials for a hands-on class called AI Tools for Developers. The course is aimed at developers who want to fold AI tools into their daily coding work. No prior machine learning experience is required, you just need basic comfort with Python or JavaScript. The course runs across six sessions. The first two sessions cover the landscape of available AI tools, how to evaluate them for a given task, and how to write prompts that produce reliable code rather than approximate guesses. Sessions three through five go deeper: structured specification-driven development, building systems that retrieve information from your own codebase or documents before generating answers (called RAG), and constructing AI agents that can call APIs, query databases, and chain tasks together. Session six is a full build project where students go from a written problem statement to a deployed application. The project involves building a text-to-SQL tool: a system where a user types a plain-English question and the AI produces a matching database query. The session covers synthetic data generation, prompt engineering for SQL, setting up a PostgreSQL database, building a full-stack web app, and deploying it with automated CI/CD. The repository is organized by lecture, with a folder for each session containing notes, a setup handbook, and demo code. There is also a projects folder for the build sessions. The README includes a brief set of ground rules, including a reminder never to commit API keys to the repository and to understand AI-generated code before using it rather than copying it blindly. This appears to be the companion repository for an instructor-led batch course rather than a standalone self-paced resource. The material references live sessions and expects students to show up with their environment already configured.

Copy-paste prompts

Prompt 1

I'm following the AI Tools for Developers course in vikasthakurr/ai-for-devs-batch-1. Help me write a prompt that reliably generates correct Python code for a given task rather than an approximate guess.

Prompt 2

Using the RAG concepts from vikasthakurr/ai-for-devs-batch-1, show me how to set up a system that searches my local codebase before generating an answer.

Prompt 3

I want to build the text-to-SQL capstone from vikasthakurr/ai-for-devs-batch-1. Walk me through generating synthetic data, writing the SQL prompt, and wiring it to a PostgreSQL database.

Prompt 4

Based on the AI agent session in vikasthakurr/ai-for-devs-batch-1, help me build an agent that can call an external API and then query a database to complete a multi-step task.

Prompt 5

I'm setting up my environment for vikasthakurr/ai-for-devs-batch-1 session one. What should I install and configure before the first class, and how do I store API keys safely without committing them to git?

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