Generative AI With LangChain Professional Program

  • High Demand In It And Automation: Generative ai is in demand because companies need professionals who can build ai tools, automate repetitive tasks, create chatbots, summarize documents, and improve business productivity.
  • Useful For Multiple Career Fields: Generative ai is useful for learners interested in ai, python development, data science, software development, automation, digital marketing, business analytics, and content technology.
  • Build Real Ai Applications: By learning generative ai, learners can create chatbots, content generators, pdf assistants, productivity tools, document q&a systems, and ai-powered portfolio projects.
2 Months ₹18,999 ₹14,999
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Generative AI with LangChain Professional Program
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Course Overview

Generative AI is a modern artificial intelligence technology where learners create AI-powered content, chatbots, document assistants, automation tools, summarization systems, and productivity applications using AI models. This 2 months course helps learners build strong practical skills in Python, AI concepts, Large Language Models, Prompt Engineering, Gemini/OpenAI model usage, LangChain, prompt templates, output parsers, chains, memory, chatbot development, document processing, RAG, vector database basics, Streamlit interface development, and portfolio-ready Generative AI projects.

Course with Live Project

No Refund Available

python and ai project development foundation: learners start with python basics, file handling, json handling, error handling, api keys, environment variables, project setup, and clean ai application structure.

langchain, chains, and structured ai workflows: work with langchain model integration, prompt templates, output parsers, reusable chains, structured responses, and multi-step ai workflows.

Chatbot, Rag, And Portfolio Projects: Develop Practical Projects Like Ai Content Generator, Chatbot With Memory, Pdf Question-answering Assistant, Resume Summary Generator, Business Report Generator, 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
  • • understanding string formatting
  • • working with user input
  • • 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
  • • creating python project folder structure
  • • understanding api keys
  • • using environment variables
  • • understanding json data format
  • • reading and writing json data
  • • handling errors using try-except
  • • managing python dependencies
  • • preparing reusable ai project templates

  • • understanding artificial intelligence and generative ai
  • • difference between ai, machine learning, deep learning, and gen ai
  • • understanding natural language processing basics
  • • introduction to large language models
  • • understanding gpt, gemini, claude, and llama models
  • • how generative ai creates text responses
  • • understanding tokens, prompts, and context windows
  • • understanding temperature and model creativity
  • • understanding hallucination and ai limitations
  • • real-world applications of generative ai

  • • understanding prompt engineering concepts
  • • writing clear and effective prompts
  • • understanding instructions, context, and output format
  • • using zero-shot prompting techniques
  • • using one-shot and few-shot prompting
  • • creating role-based prompt instructions
  • • writing step-by-step reasoning prompts
  • • creating structured output prompts
  • • creating prompt templates for common tasks
  • • prompt optimization techniques
  • • improving poor ai responses
  • • using ai for business, education, marketing, and productivity tasks

  • • 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 langchain ecosystem
  • • 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
  • • passing user input to ai models
  • • reading and processing ai responses
  • • managing model configuration

  • • understanding prompt templates in langchain
  • • creating reusable prompt templates
  • • passing dynamic inputs to prompt templates
  • • using chatprompttemplate
  • • understanding output parsers
  • • creating structured ai responses
  • • generating json-based ai outputs
  • • creating table-based ai outputs
  • • validating ai response format
  • • designing prompt templates for real applications
  • • building structured ai application flow

  • • understanding chains in langchain
  • • creating simple chains
  • • understanding langchain expression language basics
  • • combining prompt templates with models
  • • combining models with output parsers
  • • creating multi-step ai workflows
  • • passing dynamic inputs to chains
  • • creating reusable ai workflows
  • • testing ai chain responses
  • • improving chain output quality
  • • designing workflow-based ai applications

  • • understanding ai chatbot architecture
  • • understanding chat history
  • • creating context-aware conversations
  • • maintaining conversation flow
  • • designing chatbot prompts
  • • adding memory to chatbots
  • • managing previous user inputs
  • • improving chatbot response quality
  • • 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
  • • loading web-based documents
  • • splitting documents into chunks
  • • understanding chunk size and chunk overlap
  • • 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
  • • improving retrieved answers

  • • introduction to streamlit
  • • creating a simple web app layout
  • • adding text input and buttons
  • • displaying ai responses in web app
  • • creating a chatbot interface
  • • creating a pdf upload interface
  • • connecting langchain app with streamlit
  • • managing user inputs in streamlit
  • • testing ai app interface
  • • preparing local demo for portfolio

  • • create a python prompt template generator.
  • • build an ai content generation assistant using langchain.
  • • create an ai blog title and blog outline generator.
  • • build an ai email reply generator using prompt templates.
  • • create a structured resume summary generator using output parsers.
  • • build a customer support chatbot using langchain memory.

  • • streamlit-based ai chatbot app
  • • business report generator

Skills Developed with Generative AI Course

Python Programming For Ai: Learn variables, data types, operators, conditional statements, loops, functions, lists, dictionaries, file handling, json handling, error handling, and clean python scripting.
Ai Project Setup: Work with vs code, google colab, virtual environments, pip, requirements.txt, api keys, environment variables, reusable project folders, and basic project documentation.
Generative Ai Fundamentals: Understand ai, machine learning, deep learning, generative ai, natural language processing basics, 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, prompt testing, and prompt optimization techniques.
Ai Productivity Workflows: Use ai for blog writing, email replies, resume summaries, report generation, research summaries, social media content, presentations, and business productivity tasks.
Gemini And Openai Model Usage: Learn how to connect gemini or openai models for text generation, summarization, question answering, chatbot responses, structured outputs, and automation tasks.
Langchain Model Integration: Understand langchain setup, chat models, human messages, ai messages, prompt templates, user input handling, response processing, and model configuration.
Output Parsers And Structured Responses: Create structured ai outputs, json-based responses, table-based outputs, formatted summaries, and controlled response formats for real applications.
Langchain Chains And Ai Workflows: Build reusable chains by combining prompt templates, ai models, output parsers, dynamic inputs, and multi-step workflows.
Chatbot Development With Memory: Create simple and context-aware chatbots, maintain conversation flow, use chat history, improve chatbot responses, and build domain-based chatbot applications.
Document Processing And Rag: Learn document loading, pdf processing, text splitting, chunking, embeddings concept, vector database basics, retrievers, and retrieval-augmented generation workflow.
Streamlit Ai App Development: Create simple web interfaces, chatbot interfaces, pdf upload interfaces, ai response displays, and portfolio-ready demos using streamlit basics.

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 ai mini tools, preparing documentation, and assisting in ai workflow development.

Prompt Engineering Assistant:

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

Ai Chatbot Developer Beginner Role:

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

Python Ai Developer Beginner Role:

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

Ai Automation Assistant:

Use generative ai tools to automate summaries, email replies, report writing, document analysis, content creation, and business productivity tasks.

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, chains, chatbot development, rag, and ai app creation.
Practical Project-based Learning: Learners work on real-world projects like ai content generator, email reply tool, resume summary generator, chatbot with memory, 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, memory, document loaders, rag basics, vector database basics, and streamlit basics.
Mentor-guided Project Support: Learners receive mentor support for python coding, prompt writing, langchain integration, chatbot building, rag workflow, streamlit interface creation, testing, and project presentation.
Strong Foundation For Advanced Gen Ai Courses: The course builds a solid base before moving into 3 months, 4 months, or 6 months advanced 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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