STAGE 01
GenAI Foundations
Understand Generative AI, foundation models, model capabilities, limitations and common application patterns.
Build practical Generative AI capabilities from foundation models and Large Language Models through prompt engineering, embeddings, RAG, vector databases, AI agents, multimodal AI and production-ready Generative AI applications.
Generative AI has expanded artificial intelligence from traditional prediction and classification into systems capable of generating text, code, images, audio, video and other forms of digital content.
A practical Generative AI engineer needs more than prompt-writing skills. Modern GenAI development requires an understanding of foundation models, LLMs, tokens, context windows, embeddings, APIs, retrieval, vector databases and application architecture.
This learning path progresses from Generative AI foundations into LLM application development, Retrieval-Augmented Generation, AI agents, tool calling, multimodal AI and enterprise AI architectures.
The emphasis is on practical engineering: understanding how models work, integrating them with applications, connecting enterprise data and designing controlled, reliable and useful AI solutions.
Progress from Generative AI foundations to LLM applications, retrieval systems, AI agents and enterprise AI solutions.
Understand Generative AI, foundation models, model capabilities, limitations and common application patterns.
Explore foundation model concepts, training, inference, model adaptation and different generative AI modalities.
Understand LLM architecture, tokens, context, inference, capabilities, limitations and model interaction.
Design structured prompts, instructions, context and workflows for reliable model outputs.
Build practical applications using model APIs, structured outputs, function calling and application integration.
Connect LLMs with external knowledge using document processing, embeddings, vector search and retrieval pipelines.
Explore tool calling, planning, reasoning, workflows and controlled multi-step AI task execution.
Understand AI systems that work across text, images, audio, video and other data modalities.
Design production-oriented AI solutions with evaluation, security, governance, observability and enterprise integration.
A structured curriculum covering the technical foundations and application engineering practices required for modern Generative AI.
Understand the evolution of AI toward modern generative models and applications.
Learn the role of foundation models in modern Generative AI systems.
Develop a practical understanding of modern language models and their application interfaces.
Learn how to design effective prompts and structured interactions with generative models.
Build applications around language models using APIs and software engineering practices.
Understand how text and other information can be represented for semantic retrieval.
Learn the database concepts used to store and retrieve vector representations for AI systems.
Build AI applications that combine enterprise knowledge retrieval with generative models.
Explore systems that can reason through tasks, select tools and execute controlled workflows.
Understand generative systems capable of working across multiple information modalities.
Learn how Generative AI applications can be evaluated and controlled for production use.
Connect Generative AI technologies with enterprise applications, data, security and business processes.
Develop practical capabilities across LLMs, generative applications, retrieval systems, agents and enterprise AI.
Understand the concepts, models and application architectures behind modern Generative AI.
Understand foundation model capabilities, inference and application patterns.
Build applications around large language models, APIs, context and structured outputs.
Design effective instructions, context and structured prompts for AI applications.
Work with vector representations and semantic similarity for AI retrieval systems.
Build retrieval-augmented applications connected to enterprise knowledge.
Design agentic workflows using tools, planning and controlled task execution.
Understand AI applications combining text, images, audio, video and other modalities.
Project-based learning connects Generative AI concepts with practical applications and enterprise scenarios.
Build a conversational application using a language model API, prompts, context management and structured responses.
Build a knowledge assistant that processes enterprise documents, performs retrieval and generates grounded responses.
Design reusable prompt templates and evaluation workflows for business-oriented Generative AI applications.
Create an AI agent capable of selecting tools, executing multiple steps and completing controlled business tasks.
Develop a multimodal application that combines text with image or other supported data inputs.
Design an end-to-end Generative AI solution incorporating models, enterprise data, retrieval, security, evaluation and monitoring.
The learning sequence progressively moves from understanding models to building intelligent enterprise applications.
Generative AI skills can support multiple technical, architecture and leadership career paths.
Build applications using foundation models, LLMs, RAG, agents and modern GenAI technologies.
Develop applications around language models, context, embeddings, retrieval and evaluation.
Integrate generative models into software applications, APIs and enterprise workflows.
Design retrieval systems connecting enterprise knowledge and generative AI applications.
Design enterprise AI architectures integrating models, data, applications, security and platforms.
Build agentic systems capable of using tools, planning tasks and executing controlled workflows.
Continue building your AI Engineering capabilities across machine learning, deep learning and modern AI.
Learn machine learning algorithms, model development and predictive analytics.
Explore neural networks, transformers and advanced deep learning architectures.
Build end-to-end AI Engineering capabilities across data, models, applications and operations.
Explore AI agents, tool use, planning, reasoning and autonomous workflows.
A Generative AI course in India can help professionals understand how modern artificial intelligence systems generate text, code, images, audio, video and other forms of content.
Modern Generative AI engineering goes beyond basic prompting. Professionals increasingly need to understand foundation models, Large Language Models, tokens, context windows, embeddings, model APIs, vector databases and retrieval architectures.
Generative AI applications can combine models with enterprise information through Retrieval-Augmented Generation (RAG). RAG architectures allow applications to retrieve relevant information from documents, databases and knowledge repositories before generating responses.
The next stage of modern AI development involves AI agents and agentic workflows, where AI systems can use tools, interact with applications, perform multiple steps and operate under defined controls.
A comprehensive Generative AI learning path should therefore combine conceptual understanding with practical engineering. Learners should gain experience building LLM applications, RAG systems, AI agents and enterprise-oriented solutions while understanding evaluation, security, governance and responsible AI principles.
Generative AI refers to artificial intelligence systems capable of generating content such as text, code, images, audio, video and other structured or unstructured outputs.
Generative AI can be relevant for software professionals, engineers, data professionals, students, business professionals, technology leaders and experienced professionals interested in building or applying AI systems.
Yes. Large Language Models are an important area of Generative AI. The learning path covers LLM architecture, tokens, context, APIs, prompting, embeddings, retrieval and application development.
Prompt engineering is the structured design and optimization of instructions, context and inputs provided to generative AI models to achieve useful and controlled outputs.
Embeddings are numerical vector representations of information that allow AI systems to compare semantic relationships and perform similarity-based retrieval.
Retrieval-Augmented Generation combines information retrieval with a generative AI model so that an application can retrieve relevant external knowledge before generating a response.
Vector databases are specialized data systems used to store, index and retrieve vector representations for similarity and semantic search applications.
Agentic AI refers to AI systems designed to perform multi-step tasks using capabilities such as planning, reasoning, tool usage and controlled execution.
Yes. The learning path introduces multimodal Generative AI concepts involving combinations of text, images, audio, video and other modalities.
Yes. Practical projects can include LLM applications, enterprise RAG assistants, AI agent workflows, multimodal applications and enterprise Generative AI architectures.
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