Generative AI
This intensive training focuses on the practical deployment of cutting-edge AI models for tasks like content generation, code creation, and complex problem-solving. You will gain expertise in tuning and integrating large language models, earning a certification in one of the most transformative technologies of the decade.
The Certificate course for you!
Prompt engineering patterns: To get consistent, controllable outputs from LLMs.
RAG pipelines: Chunking, embeddings, evaluation, and guardrails for accuracy.
Agents & function calling: To integrate tools, APIs, and workflows.
What Will You Learn?
LLM foundations & prompting: System prompts, few-shot, chain-of-thought (responsibly), and evaluation.
RAG: Text splitting, vector DB basics, retrieval strategies, reranking, and quality checks.
Function calling & agents: Tool use, API orchestration, and background jobs with guardrails.
Responsible AI: Safety filters, prompt injection defenses, PII handling, and monitoring.
Course Curriculum
- Understanding Generative AI 0:02 Min
- Generative Models and Architectures 0:03 Min
- Applications of Generative Ai 0:03 Min
- Basic concepts and applications of Generative AI. 0:25 Min
- Generative Models and Architectures – Generative Adversarial Networks (GANs) 0:03 Min
- Variational Autoencoders 0:03 Min
- Transformers and Large Language Models 0:03 Min
- Comparing different generative models and their use cases. 0:25 Min
- Data Preparation and Preprocessing 0:02 Min
- Building and Training Generative Models – Training GANs and VAEs 0:03 Min
- Fine-Tuning Pre-trained Models 0:03 Min
- Building and Training Generative Models 0:15 Min
- Generative AI in Healthcare and Research 0:05 Min
- Generative AI in Automation and Customer Experience 0:05 Min
- Implementing Generative AI in Real-World Scenarios 0:15 Min
- Ethical Implications and Bias in Generative AI 0:05 Min
- Ethics, Challenges, and Future of Generative AI – Regulatory and Privacy Concerns 0:05 Min
- Future Trends in Generative AI 0:04 Min
- Ethical considerations and future advancements in Generative AI. 0:25 Min
Learn With GreyLearn
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Adapt models: RAG pipelines, fine-tuning/LoRA, vector stores, and domain grounding.
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Build apps end-to-end: APIs, orchestration (LangChain/LlamaIndex), tools, and multimodal inputs/outputs.
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Evaluate and ship safely: Quality metrics, bias and safety checks, monitoring, and latency/cost optimization.
What Learners Say
Supriya Mahesh
This course made generative ai practical for real work i use small checks to keep outputs on topic the course showed realistic limits and tradeoffs
Varsha Rani
The course balanced creativity and responsible use this course made generative ai practical for real work i learned when to escalate tasks to human editors
Rohit Nile
The approach fits product design research and support the style guide keeps tone steady across responses the checkpoints keep experiments organized and honest
Himanshu Dwivedi
I can prototype ideas fast without losing control examples covered code text and simple data tasks i learned prompt patterns that give stable reliable output
Yash Verma
The approach scales from a draft to a working system i connect tools to chain prompts for end to end tasks i can combine search
Ayush Saini
Evaluations helped me measure quality not just speed i use schemas so tools can read outputs reliably the planner breaks big goals into verified steps
Frequently Asked Questions
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