Generative AI Essentials

  • High Demand In It And Business: Generative ai is in demand because companies need professionals who can use ai tools, automate workflows, build chatbots, summarize documents, and create ai-powered applications.
  • Useful For Multiple Career Paths: Generative ai is useful for learners interested in ai, python development, data science, automation, digital marketing, content creation, business analytics, and software development.
  • Build Real Ai Applications: By learning generative ai, learners can create chatbots, content tools, document assistants, productivity tools, and ai-powered mini applications for portfolio building.
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Course Overview

Generative AI is a modern artificial intelligence technology where learners create AI-powered content, chatbots, summaries, document assistants, automation tools, and productivity applications using AI models. This 1 month course helps learners build a strong foundation in Python basics, AI concepts, Large Language Models, Prompt Engineering, Gemini/OpenAI model usage, LangChain, prompt templates, AI chatbot development, document question-answering, RAG basics, and beginner-to-intermediate Generative AI projects.

Course with Live Project

No Refund Available

Python And Generative Ai Foundation: Learners Start With Python Basics, Ai Fundamentals, Large Language Models, Tokens, Prompts, Context Windows, And Real-world Generative Ai Use Cases.

Prompt Engineering And Langchain Skills: Work With Role-based Prompts, Structured Prompts, Prompt Templates, Gemini/openai Model Integration, Langchain Chains, And Basic Ai Workflows.

Project-based Gen Ai Development: Develop Practical Projects Like Ai Content Generator, Ai Email Reply Tool, Ai Chatbot, Resume Summary Generator, Pdf Question-answering Assistant, And Generative Ai Knowledge Assistant.

Course Content

  • • Python installation and environment setup
  • • Introduction to python for ai development
  • • Understanding variables and data types
  • • Working with strings and text data
  • • Using python operators
  • • Using conditional control statements
  • • Applying loops for repeated tasks
  • • Creating functions for reusable code
  • • Working with lists and tuples
  • • Working with dictionaries and sets
  • • Working with files in python
  • • Writing clean python scripts for ai tasks

  • • Setting up vs code and google colab
  • • Creating python virtual environment
  • • Installing python packages
  • • Understanding pip and requirements.txt
  • • Understanding api keys
  • • Using environment variables
  • • Understanding json data format
  • • Handling errors in python
  • • Structuring a basic ai project folder

  • • Understanding prompt engineering
  • • Writing clear and effective prompts
  • • Using role-based prompting
  • • Using zero-shot prompting
  • • Using one-shot and few-shot prompting
  • • Using structured output prompts
  • • Creating prompt templates for repeated tasks
  • • Improving poor ai responses
  • • Using ai for business, education, marketing, and productivity tasks

  • • Introduction to langchain
  • • Why langchain is used in generative ai
  • • Installing langchain
  • • Connecting gemini model with langchain
  • • Connecting openai model with langchain
  • • Understanding chat models
  • • Understanding human messages and ai messages
  • • Creating prompt templates in langchain
  • • Passing user input to ai models
  • • Reading and processing ai responses
  • • Using output parsers for structured output

  • • Understanding chains in langchain
  • • Creating simple chains
  • • Understanding langchain expression language basics
  • • Combining prompt templates with models
  • • Using output parsers with chains
  • • Creating multi-step ai workflows
  • • Passing dynamic inputs to chains
  • • Testing ai chain responses
  • • Improving chain output quality

  • • Understanding ai chatbot architecture
  • • Understanding chat history
  • • Creating context-aware conversations
  • • Maintaining conversation flow
  • • Designing chatbot prompts
  • • Improving chatbot responses
  • • Handling wrong or irrelevant responses
  • • Testing chatbot conversations

  • • Understanding document-based ai applications
  • • Introduction to retrieval-augmented generation
  • • Loading text files and pdf documents
  • • Splitting documents into chunks
  • • Understanding vector databases
  • • Using facebook ai similarity search (faiss) or chroma basics
  • • Creating a retriever
  • • Connecting retriever with llm
  • • Building a document question-answering system
  • • Testing rag responses

  • • Creating a simple web-based interface
  • • Introduction to streamlit for ai apps
  • • Connecting langchain app with frontend
  • • Testing ai applications
  • • Debugging common errors
  • • Writing project documentation
  • • Preparing portfolio presentation

  • • Create a python prompt template generator
  • • Build an ai content generation assistant using langchain
  • • Create a gemini/openai chatbot using langchain
  • • Build a pdf question-answering system using rag
  • • Create an ai productivity assistant for summary and email reply generation

  • • Ai resume summary generator

Skills Developed with Generative AI Course

Python Basics For Ai: Learn variables, data types, operators, conditional statements, loops, functions, lists, dictionaries, file handling, and clean python scripting.
Generative Ai Fundamentals: Understand ai, machine learning, deep learning, generative ai, large language models, tokens, prompts, context windows, and ai limitations.
Prompt Engineering: Learn zero-shot prompting, one-shot prompting, few-shot prompting, role-based prompting, structured prompts, and prompt improvement techniques.
Ai Content Creation: Use ai for blog writing, email generation, resume summaries, report writing, social media content, idea generation, and productivity tasks.
Gemini And Openai Model Usage: Learn how to connect gemini or openai models for text generation, question answering, summarization, and chatbot responses.
Langchain Basics: Understand langchain, model integration, prompt templates, user input handling, output processing, and simple ai application workflows.
Langchain Chains: Create basic chains by combining prompt templates, ai models, and output responses for reusable ai workflows.
Ai Chatbot Development: Build simple ai chatbots with user input, prompt handling, response generation, and basic conversation flow.
Document Question-answering And Rag Basics: Learn document loading, text splitting, embeddings concept, vector database basics, retriever concept, and basic rag workflow.
Gen Ai Project Development: Practice planning, coding, testing, debugging, documenting, and presenting beginner-to-intermediate generative ai projects.

Career Opportunities after Generative AI Course

This course opens doors to multiple high-demand career paths across industries.

Generative Ai Intern:

Support ai teams by creating prompts, testing ai responses, building small ai tools, and documenting ai workflows.

Prompt Engineering Assistant:

Create and improve prompts for content generation, chatbot responses, summaries, reports, and structured outputs.

Ai Chatbot Developer Beginner Role:

Build and test simple ai chatbots for education, customer support, productivity, and business use cases.

Python Ai Developer Beginner Role:

Create small python-based ai applications using langchain, gemini/openai models, prompt templates, and basic rag workflows.

Ai Automation Assistant:

Use generative ai tools to automate summaries, email replies, reports, document analysis, and productivity workflows.

Why Enroll in Generative AI with Solitaire Learning?

Beginner-friendly Ai Training: The course starts from python basics and ai fundamentals, making it suitable for learners from different backgrounds.
Practical Project-based Learning: Learners work on real-world projects like ai content generator, chatbot, email reply tool, resume summary generator, and pdf question-answering assistant.
Industry-relevant Ai Tools: The course covers python, chatgpt, google gemini, openai/gemini api basics, langchain, prompt templates, document loaders, and rag fundamentals.
Mentor-guided Project Support: Learners receive mentor support for python concepts, prompt writing, langchain integration, chatbot creation, document assistant development, and project presentation.
Strong Foundation For Advanced Gen Ai Courses: The course builds a solid base before moving into 45 days, 2 months, 3 months, 4 months, or 6 months generative ai programs.

Ready to Take the Next Step in Your Career?

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