Build practical Generative AI skills through prompt engineering, LLMs, AI APIs, RAG, automation, and AI application concepts.
Learn Generative AI step by step through hands-on exercises, practical use cases, and six capstone projects covering AI content generation, chatbots, document Q&A, RAG, data processing, and AI automation workflows.
Course features, batch schedules, certification details, EMI eligibility, placement support, and other terms are subject to Codelyra's current offering.
Generative AI Workflow
Prompt → Context → Model → Response
Create a structured summary using the provided context and return the result in a clear format.
Follow a structured learning path from Artificial Intelligence and Generative AI fundamentals to prompt engineering, Large Language Models, APIs, embeddings, RAG, AI automation, agents, application development, evaluation, and practical capstone projects.
Build the foundation for Generative AI, AI tools, prompt engineering, and structured interaction with AI systems.
Learning Outcome: Understand fundamentals of AI and Generative AI and identify common use cases.
Learning Outcome: Understand how Gen AI tools support common knowledge and productivity tasks.
Learning Outcome: Create clearer, more structured prompts for different AI tasks.
Learning Outcome: Design, test and refine structured prompts for different Generative AI workflows.
Move from prompting into LLM concepts, AI APIs, embeddings, vector databases, RAG and conversational AI workflows.
Learning Outcome: Understand key concepts behind LLM-powered applications.
Learning Outcome: Understand how applications communicate with AI models through APIs.
Learning Outcome: Understand how embeddings represent and retrieve information based on meaning.
Learning Outcome: Understand the role of vector databases in semantic search and Generative AI applications.
Learning Outcome: Understand how RAG connects external knowledge with LLM responses.
Learning Outcome: Understand the components required for AI conversational applications.
Explore AI automation, agents, application architecture, evaluation, responsible AI, business case studies, and end-to-end project development.
Learning Outcome: Design workflows combining Generative AI with automation.
Learning Outcome: Understand core AI agent and tool-enabled workflow concepts.
Learning Outcome: Understand how components combine into maintainable Generative AI applications.
Learning Outcome: Evaluate AI outputs and improve prompts, retrieval and application workflows.
Learning Outcome: Understand responsible AI usage and design considerations.
Learning Outcome: Apply Generative AI concepts to practical business scenarios.
Learning Outcome: Develop an end-to-end Generative AI application workflow.
Learning Outcome: Complete a practical Generative AI capstone project by combining prompting, LLMs, APIs, data, RAG, automation, testing and evaluation concepts.
The syllabus is designed as a progressive learning journey, helping learners move from understanding Generative AI concepts to designing prompts, integrating AI models, working with retrieval, building workflows, evaluating outputs, and applying the concepts through projects.
Explore the learning structure, topics, and learner profile for Codelyra's Gen AI training in Chennai. The course progresses from Generative AI fundamentals to prompt engineering, LLMs, RAG, automation, and practical AI applications.
Batch Overview
Build your understanding progressively across core Generative AI concepts, prompting, models, retrieval, automation, and application workflows.
Current batch dates, timings, learning format, availability, fees, and enrollment terms should be confirmed with Codelyra before registration.
Generative AI is more than simply using an AI chatbot. Codelyra's Gen AI training in Chennai follows a structured path covering prompts, language models, APIs, embeddings, retrieval, automation, and AI application workflows—so learners can understand how these concepts connect in practical scenarios.
Progress from AI and Generative AI fundamentals to prompt engineering, LLMs, APIs, RAG, automation, and AI application concepts.
Core concepts are introduced progressively before moving into more technical topics such as APIs, embeddings, retrieval, and automation.
Learn how to structure instructions, provide context, define roles, use examples, specify outputs, and refine prompts for different tasks.
Explore tokens, context windows, model inputs and outputs, parameters, inference concepts, and common LLM limitations.
Understand how applications interact with AI models through API requests, responses, JSON, parameters, and integration workflows.
Learn the fundamentals of embeddings, semantic search, vector databases, retrieval, context injection, and RAG workflows.
Explore AI-powered workflows for content processing, information extraction, classification, summarization, and repetitive tasks.
Understand agents, tool use, function calling concepts, context management, planning, and multi-step AI workflows.
Apply Generative AI concepts through six projects covering content, customer support, RAG, document analysis, data summarization, and automation.
The curriculum connects foundational concepts with practical AI workflows and application development.
Flexible timing, certification details, EMI eligibility, placement support, and other course terms are subject to Codelyra's current offering. Placement support refers to career-oriented assistance and does not constitute a guarantee of employment.
As part of the Gen AI Training in Chennai at Codelyra, you will work with relevant Generative AI tools and technologies to understand prompting, AI APIs, application development, embeddings, retrieval, automation, and practical AI workflows.
Explore conversational AI for prompt engineering, content generation, summarization, analysis, structured outputs, and practical AI-assisted workflows.
Learn how conversational Generative AI can support prompting, content generation, structured responses, and common AI-assisted workflows.
Understand API requests and responses, JSON, prompt submission, model interaction, response handling, and AI application integration concepts.
Use Python as a programming foundation for AI API integration, data processing, automation, experimentation, and Generative AI application concepts.
Work with an interactive environment for Python experiments, data exploration, API exercises, prototyping, and practical Generative AI workflows.
Understand the role of open-source AI and machine learning resources, including models, datasets, and model-based workflows.
Learn the concepts behind embeddings, vector storage, metadata, similarity search, and retrieval workflows used in Generative AI applications.
Understand repositories, commits, branches, and change tracking as part of managing practical Generative AI project work.
Learn how to use AI tools for business, marketing, automation, productivity, and content creation through live practical sessions and real-world use cases.
Improve communication, logical thinking, confidence, and interview performance through dedicated soft skills and aptitude sessions.
The Gen AI training in Chennai at Codelyra covers the core skills needed to understand, design, integrate, test, and apply Generative AI workflows—from prompt engineering and LLMs to RAG, automation, AI agents, and application development.
Understand AI, Generative AI, common use cases, model inputs and outputs, limitations, hallucination concepts, and responsible AI.
Learn to structure instructions, provide context, define roles, use examples, specify outputs, and create reusable prompt templates.
Practice prompt decomposition, multi-step prompting, structured outputs, evaluation, testing, refinement, and reusable prompt design.
Explore tokens, tokenization, context windows, model parameters, inputs and outputs, inference concepts, and LLM limitations.
Work with text generation, summarization, rewriting, classification, information extraction, brainstorming, and structured AI outputs.
Understand API requests and responses, JSON, parameters, model interaction, structured outputs, error handling, and integration workflows.
Understand vector representations, text embeddings, semantic similarity, embedding generation, and embedding-based applications.
Learn how meaning-based retrieval works using query embeddings, document embeddings, similarity search, metadata, and retrieval workflows.
Explore vector storage, metadata, indexing concepts, similarity search, filtering, query vectors, document vectors, and retrieval pipelines.
Understand document ingestion, chunking, embeddings, retrieval, context injection, prompt construction, RAG pipelines, and evaluation concepts.
Learn chatbot architecture, conversation history, context handling, prompt templates, LLM integration, APIs, response handling, and testing.
Design AI-powered workflows for text processing, summarization, classification, information extraction, document processing, and automation.
Understand agents, tool use, function calling concepts, planning, context management, multi-step tasks, and agent evaluation.
Understand application architecture across prompts, models, APIs, data, retrieval, validation, error handling, testing, and documentation.
Assess relevance, consistency, grounded responses, retrieval quality, response quality, hallucinations, and defined evaluation criteria.
Learn about privacy, data handling, hallucinations, bias concepts, human oversight, output verification, secure usage, and responsible AI practices.
The learning path connects foundational knowledge with increasingly practical Generative AI workflows and application concepts.
The course combines conceptual learning with practical exercises, use cases, testing, evaluation, and project workflows to help learners understand how Generative AI components work together.
Six capstone projects provide opportunities to apply prompt engineering, LLMs, APIs, RAG, data processing, AI automation, and application workflow concepts.
Apply the concepts covered in the Gen AI Training in Chennai course through six practical capstone projects. Each project brings together relevant Generative AI concepts, tools, prompts, APIs, retrieval, automation, testing, and evaluation.
Build a structured AI-assisted content workflow for generating, rewriting, summarizing, and refining marketing content using well-designed prompts.
Skills & Concepts AppliedCreate an AI-powered conversational support workflow that explores prompts, conversation context, API interaction, response handling, and output evaluation.
Skills & Concepts AppliedDevelop a document-based question-answering workflow that connects external document knowledge with an LLM through processing, embeddings, retrieval, and RAG concepts.
Skills & Concepts AppliedBuild a structured text-analysis workflow that uses Generative AI concepts to extract information, classify text, produce structured outputs, and evaluate results.
Skills & Concepts AppliedCombine Python-based data processing with Generative AI concepts to create structured summaries, process information, and support automated reporting workflows.
Skills & Concepts AppliedExplore a multi-component AI workflow that combines automation, agent fundamentals, tool use, function calling concepts, APIs, context management, and evaluation.
Skills & Concepts AppliedThe Gen AI Training in Chennai at Codelyra combines a structured learning path with practical Generative AI concepts, hands-on capstone projects, and learner-focused course support.
Learn through a schedule designed to provide flexibility around your learning routine. Available timing options can be confirmed with Codelyra.
The course includes opportunities for 2 or more certification credentials, subject to the current certification structure and eligibility terms.
EMI payment options may be available to help learners manage course payments. Eligibility and applicable terms can be confirmed with Codelyra.
Receive career-oriented placement support as part of the course. Placement assistance does not represent a guaranteed job offer.
Apply Generative AI concepts through six practical capstone projects covering content generation, RAG, chatbots, analysis, automation, and AI agent workflows.
Build practical understanding across the Generative AI workflow, from prompt design and LLM fundamentals to retrieval, automation, testing, and application development.
Earn an industry-recognized certification that validates your skills in Playwright, Automation Testing, API Testing & real-world projects. Complete hands-on projects and assessments to become job-ready in QA automation.
Build a strong profile with automation projects, frameworks, and certification that attract recruiters.
Work on 10+ automation projects & capstone projects to build a job-ready testing portfolio.
Find clear answers to common questions about the Generative AI course, learning path, tools, projects, certification, placement support, and eligibility at Codelyra.
Gen AI training teaches the concepts and practical workflows behind Generative AI, including AI fundamentals, prompt engineering, Large Language Models, AI APIs, embeddings, vector databases, RAG, automation, AI agents, and AI application development.
Yes. The learning path is structured from beginner-level Artificial Intelligence and Generative AI fundamentals before progressing to prompt engineering, LLMs, APIs, RAG, automation, AI agents, and application concepts.
Programming experience is not required to understand the foundational Generative AI concepts. The course progressively introduces technical areas such as Python, APIs, data processing, embeddings, retrieval, and AI application development.
The course covers Generative AI fundamentals, prompt engineering, LLM fundamentals, AI APIs, embeddings, semantic search, vector databases, RAG, AI chatbots, automation, AI agents, application development, testing, evaluation, responsible AI, and practical capstone projects.
Prompt engineering is the practice of designing clear and structured instructions for Generative AI models. It can include defining the task, providing context, assigning roles, adding examples, specifying output formats, and refining prompts based on the quality of the results.
Yes. The course covers LLM fundamentals such as tokens, tokenization, context windows, model inputs and outputs, parameters, inference concepts, training and fine-tuning concepts, model limitations, and LLM application architecture.
Yes. The course introduces AI API concepts including API requests and responses, JSON, request parameters, model interaction, response handling, error handling, API integration, and application integration concepts.
Yes. The course covers Retrieval-Augmented Generation, including document ingestion, preprocessing, text chunking, embeddings, vector storage, retrieval, context injection, prompt construction, response generation, and RAG evaluation concepts.
Embeddings are vector representations used to represent information in a form that supports similarity-based retrieval. The course covers text embeddings, semantic similarity, query and document embeddings, and embedding-based retrieval workflows.
Yes. You will explore vector database concepts including vector and embedding storage, metadata, similarity search, indexing concepts, filtering, query vectors, document vectors, and retrieval workflows.
The course covers AI chatbot fundamentals such as chatbot architecture, conversation flow, user input, system instructions, conversation history, context handling, prompt templates, LLM integration, API integration, response handling, testing, and evaluation.
Yes. Generative AI automation concepts include AI-powered workflows, content processing, summarization, classification, information extraction, data transformation, document processing, API-based automation, multi-step workflows, and automation testing.
Yes. The course introduces AI agent fundamentals, including agent architecture, goals and instructions, tool use, function calling concepts, external tools, context management, planning concepts, multi-step tasks, agent workflows, and evaluation.
The course includes 6 capstone projects covering different Generative AI workflows, including content generation, customer support, RAG-based document Q&A, text analysis, data summarization and reporting, and AI automation with agent workflow concepts.
The six projects are: AI Content Generation & Marketing Assistant, AI Customer Support Assistant, Document Q&A with RAG, AI Resume & Job Description Analyzer, AI-Powered Data Summarization & Reporting, and AI Automation & Agent Workflow.
The course includes certification opportunities. The current certification structure, eligibility requirements, and issuing details should be confirmed with Codelyra before enrollment.
The program is designed around Generative AI concepts, practical learning, and capstone projects, with 2+ certification opportunities listed as a course feature. Certification details and applicable terms can be confirmed with Codelyra.
Placement support is included as a course feature. This refers to career-oriented assistance and does not represent a guaranteed job offer or employment outcome.
Yes, placement support is listed among the course features. Specific support services, eligibility, process, and current terms should be confirmed directly with Codelyra.
The course can be relevant for students, graduates, software professionals, developers, data analysts, digital marketers, content professionals, HR professionals, business professionals, product and business analysts, automation enthusiasts, and working professionals interested in Generative AI applications.
The course content includes tools and technologies such as ChatGPT, Google Gemini, OpenAI API, Python, Jupyter Notebook, Hugging Face, vector database technology, and Git. Exact tool availability, versions, API access, and implementation details may vary and can be confirmed with Codelyra.
Many Generative AI tools can be used for common tasks without programming. However, the course also introduces technical concepts such as Python, APIs, embeddings, vector databases, automation, and AI application development for learners who want to understand how more advanced AI workflows are built.
The learning path progresses from AI and Generative AI fundamentals to prompt engineering and LLMs, followed by APIs, embeddings, vector databases, RAG, chatbots, automation, AI agents, application development, testing, evaluation, and practical capstone projects.
Codelyra's learning path focuses on structured Generative AI concepts, prompt engineering, LLM fundamentals, AI APIs, RAG, automation, AI agent fundamentals, application development, testing, evaluation, and six practical capstone projects.
