Generative AI course in Mohali

You will begin by understanding the foundation of Generative AI and how Large Language Models (LLMs) work. This gives you the technical context needed to understand why AI models generate particular responses and how they can be used effectively in applications.

You will explore concepts such as AI-generated text, model capabilities, limitations, context, tokens, and practical LLM workflows. The aim is to move beyond simply asking questions to AI tools and understand the technology powering modern AI applications.

Prompt engineering is an important part of working with Generative AI. You will learn how to create structured prompts, improve instructions, provide context, control outputs, and evaluate whether a prompt is actually producing reliable results.

The course focuses not only on writing prompts but also on testing and judging prompt quality, helping you develop a more systematic approach to AI interaction and application development.

The course provides practical exposure to popular AI platforms and model ecosystems, including OpenAI/ChatGPT and Claude. You learn how these models can be integrated into practical workflows and applications instead of using them only through their consumer interfaces.

This can help students understand model selection, API-based interaction, prompt workflows, and the difference between simply using an AI assistant and incorporating an AI model into a software solution.

You will also explore open-source AI models through Hugging Face. This introduces students to an ecosystem of models and tools that can be used for experimentation and application development.

Learning Hugging Face alongside commercial AI APIs gives students a broader understanding of the Generative AI landscape and how different model options can be incorporated into applications.

Python is one of the core technologies used throughout the program. You will learn how Python can be used to connect AI models, process data, build application logic, and create AI-powered solutions.

The course moves from concepts toward building practical applications with Python, giving students an opportunity to combine programming knowledge with Generative AI capabilities.

One of the important technical areas covered is embeddings and vector databases. Embeddings allow information to be represented in a form that AI systems can use for similarity-based retrieval.

You will learn the role of vector databases and how they can store and retrieve relevant information efficiently. The Techcadd curriculum specifically includes Pinecone as one of the tools used in this area.

RAG, or Retrieval-Augmented Generation, is a major practical component of the course. You will learn how to build systems that retrieve relevant information from a user's documents or knowledge base before generating an answer.

This is particularly useful when developing AI assistants that need to work with business documents, internal knowledge, FAQs, research material, or other customized information. Instead of relying only on a model's pretrained knowledge, RAG allows the application to retrieve relevant external information before responding.

The course introduces LangChain for developing AI-powered applications. You will learn how frameworks like LangChain can connect models, prompts, retrieved information, tools, and application workflows.

This helps students understand the architecture behind practical AI applications rather than treating an LLM as an isolated chatbot.

Generative AI is not limited to text. The course also covers image and audio generation, giving students exposure to multimodal possibilities within the AI ecosystem.

This can be particularly useful for learners interested in content creation, marketing automation, creative technology, media applications, and AI-powered digital products.

You will work with Streamlit to turn AI functionality into usable applications. Instead of keeping your work inside notebooks or scripts, Streamlit can help you create an accessible interface around your AI project.

This makes the learning process more portfolio-oriented because students can demonstrate an actual working application instead of showing only code snippets.

Another important skill is understanding how AI models can be integrated through APIs. You will learn the practical workflow of connecting AI services with Python applications and using model capabilities inside software solutions.

This is an important step toward developing AI-based automation tools, assistants, productivity applications, and business-focused solutions.

The program does not stop at creating an AI prototype. The curriculum also covers cost, safety, and deployment, which are essential considerations when taking an AI application beyond experimentation.

You will gain awareness of issues such as responsible AI usage, application costs, deployment considerations, and the challenges that can arise when moving an AI prototype toward a usable system.

A major practical component is project work. Techcadd states that students work through live client projects under trainer supervision, with every stage producing something that can be reviewed and improved. The course culminates in a deployed AI application, portfolio development, CV preparation, and interview practice.