Generative AI Professional Program

  • High Demand In It And Automation: Generative ai is in demand because companies need professionals who can build ai tools, automate workflows, create chatbots, summarize documents, and improve productivity.
  • Useful For Multiple Career Fields: Generative ai is useful for learners interested in ai, python development, automation, content creation, digital marketing, business analytics, data science, and software development.
  • Build Real Ai-powered Applications: By learning generative ai, learners can create chatbots, document assistants, content tools, productivity apps, and basic rag-based applications for portfolio building.
1.5 Months ₹11,999 ₹8,999
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Generative AI Professional Program
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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 45 days course helps learners build practical skills in Python basics, AI concepts, Large Language Models, Prompt Engineering, Gemini/OpenAI model usage, LangChain, prompt templates, output parsers, chains, memory, chatbot development, document processing, RAG basics, Streamlit basics, and portfolio-ready Generative AI projects.

Course with Live Project

No Refund Available

Python And Ai Application Foundation: Learners Start With Python Basics, Project Setup, Files, Json, Error Handling, Api Keys, Environment Variables, And Clean Ai Project Structure.

Langchain And Prompt Engineering Skills: Work With Prompt Templates, Structured Prompts, Output Parsers, Gemini/openai Model Integration, Chains, And Reusable Ai Workflows.

Chatbot, Rag, And Project Development: Develop Practical Projects Like Ai Content Generator, Chatbot With Memory Basics, Pdf Question-answering Assistant, Ai Email Reply Tool, 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
  • • Reading json data in python
  • • Handling errors using try-except
  • • Structuring a basic ai project folder

  • • Understanding artificial intelligence and generative ai
  • • Difference between ai, ml, deep learning, and gen ai
  • • Understanding natural language processing basics
  • • Introduction to large language models
  • • Understanding gpt, gemini, claude, and llama models
  • • Understanding tokens, prompts, and context windows
  • • Understanding temperature and model creativity
  • • Understanding hallucination and ai limitations
  • • Writing clear and effective prompts
  • • Using zero-shot, one-shot, and few-shot prompting
  • • Creating role-based prompt instructions
  • • Creating structured output prompts
  • • Optimizing poor ai responses

  • • Using chatgpt for prompting and content creation
  • • Using chatgpt for file analysis and summarization
  • • Using chatgpt for productivity tasks
  • • Using google gemini for research and content tasks
  • • Using ai for blog writing
  • • Using ai for resume and portfolio building
  • • Using ai for presentation and document creation
  • • Exploring ai image generation basics
  • • Exploring ai video generation basics
  • • Understanding responsible use of ai tools

  • • Introduction to langchain
  • • Why langchain is used in generative ai applications
  • • Installing and setting up langchain
  • • Understanding api keys and environment variables
  • • 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
  • • Creating reusable ai workflows

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

  • • Understanding document-based ai applications
  • • Introduction to retrieval-augmented generation
  • • Loading text files and pdf documents
  • • Splitting documents into chunks
  • • Understanding embeddings
  • • Understanding vector representation
  • • 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
  • • Improving retrieved answers

  • • Introduction to streamlit
  • • Creating a simple web app layout
  • • Adding text input and buttons
  • • Displaying ai responses in web app
  • • Connecting langchain app with streamlit
  • • Creating chatbot interface
  • • Creating pdf upload interface
  • • Testing ai app interface
  • • Preparing local demo for portfolio

  • • Create a python prompt template generator.
  • • Build an ai content generation assistant using langchain.
  • • Create a gemini/openai chatbot using langchain.
  • • Build a blog generator chain using prompt templates and output parsers.
  • • Create a customer support chatbot with memory.

  • • Pdf question answering assistant

Skills Developed with Generative AI Course

Python Basics For Ai: Learn variables, data types, operators, conditional statements, loops, functions, lists, dictionaries, files, json handling, and clean python scripting.
Generative Ai Fundamentals: Understand ai, machine learning, deep learning, generative ai, large language models, tokens, prompts, context windows, temperature, and ai limitations.
Prompt Engineering: Learn zero-shot prompting, one-shot prompting, few-shot prompting, role-based prompts, structured prompts, reusable prompt templates, and prompt optimization.
Ai Productivity Workflows: Use ai for blog writing, email replies, resume summaries, report generation, research summaries, presentations, social media content, and business tasks.
Gemini And Openai Model Usage: Learn how to connect gemini or openai models for text generation, summarization, question answering, chatbot responses, and structured outputs.
Langchain Model Integration: Understand langchain setup, chat models, human messages, ai messages, prompt templates, user input handling, and response processing.
Langchain Chains And Output Parsers: Create reusable chains, structured ai outputs, json-based responses, table-based outputs, and multi-step ai workflows. ai chatbot development: build simple ai chatbots, context-aware conversations, memory-based chatbot basics, customer support bots, and study assistant bots.
Document Question-answering And Rag: Learn document loading, pdf processing, text splitting, embeddings concept, vector database basics, retrievers, and basic rag workflow.

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, business reports, summaries, and structured outputs.

Ai Chatbot Developer Beginner Role:

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

Python Ai Developer Beginner Role:

Create python-based ai applications using langchain, gemini/openai models, prompt templates, chains, and rag basics.

Ai Automation Assistant:

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

Why Enroll in Generative AI with Solitaire Learning?

Beginner-to-intermediate Ai Training: The course starts from python basics and gradually moves toward prompt engineering, langchain, chatbots, rag, and ai project development.
Practical Project-based Learning: Learners work on real-world projects like ai content generator, email reply tool, chatbot, customer support assistant, and pdf question-answering assistant.
Industry-relevant Ai Tools: The course covers python, chatgpt, google gemini, openai/gemini api basics, langchain, prompt templates, output parsers, chains, document loaders, rag basics, and streamlit basics.
Mentor-guided Project Support: Learners receive mentor support for python coding, prompt writing, langchain integration, chatbot creation, document assistant development, testing, and project presentation.
Strong Foundation For Advanced Gen Ai Courses: The course builds a solid base before moving into 2 months, 3 months, 4 months, or 6 months generative ai programs.

Ready to Take the Next Step in Your Career?

Join our expert-led training program, gain industry-recognized skills, and move closer to your professional goals. Seats are limited — enroll today!

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