Generative AI is a branch of artificial intelligence that creates new content — text, images, code, audio, or video — instead of only classifying or predicting existing data. It works through large models, mostly built on the transformer architecture, that are trained on massive datasets to predict the next word or token. When you give a prompt, the model generates a response step by step based on patterns learned during training, which is why tools like ChatGPT can write, summarize, and code conversationally.

Generative AI refers to models that create content from prompts, such as writing text, generating code, or producing images. Agentic AI goes a step further — it uses LLMs as the reasoning engine for agents that can plan tasks, use tools, call APIs, and complete multi-step goals with minimal human input. In short, generative AI creates output, while agentic AI acts on your behalf; frameworks like LangChain, CrewAI, and Google ADK are commonly used to build such agents.

Start with Python, then build on the core GenAI stack: LangChain and LlamaIndex for RAG pipelines, CrewAI or Google ADK for agents, and vector databases like Chroma, Milvus, or Elasticsearch for retrieval. Add PyTorch or TensorFlow if you want deeper model-level skills, and FastAPI or Flask to deploy your apps as APIs. Building two or three end-to-end projects with these generative AI tools matters far more than collecting tool names on a resume.

Judge a generative AI course on four things: whether it includes hands-on projects (a working RAG app or a fine-tuned model, not just slides), whether the instructor has real industry experience building production AI systems, whether the syllabus is current (LLMs, RAG, agents, deployment), and whether you get doubt-clearing or mentorship support. Watching a free live session or webinar by the instructor is the easiest way to judge the teaching style before you pay.

Yes. Platforms like IBM SkillsBuild, Google, Microsoft Learn, and DeepLearning.AI offer free generative AI courses that award a certificate on completion. A free generative AI course with certificate is a great low-risk starting point, but recruiters in India weight projects more than certificates, so pair it with hands-on work such as building a small RAG chatbot.

It depends on your goal. For enterprise and cloud roles, credentials like IBM watsonx certifications, Azure AI and Azure Data Engineering, and Google Cloud AI paths are well recognized by Indian employers, while entry-level options such as IBM's Generative AI Leader certification suit beginners. A generative AI certification helps your resume clear filters, but interviews test practical skills — RAG, fine tuning, deployment — so always back it with projects.

Retrieval augmented generation (RAG) is a technique where an LLM first retrieves relevant documents from your knowledge base using embeddings and a vector database, then generates its answer grounded in that retrieved context. It reduces hallucinations, lets a model use private or frequently updated data without retraining, and is the standard architecture behind enterprise chatbots that answer from company documents.

The basic pipeline: collect and chunk your documents, convert the chunks into embeddings, and store them in a vector database like Chroma, Milvus, or Elasticsearch. At query time, retrieve the top-matching chunks, add them to the prompt as context, and let the LLM generate the answer. Frameworks like LangChain and LlamaIndex speed up implementation, FastAPI can expose it as an API, and you improve quality by tuning chunk size, top-k, and reranking.

They solve different problems. RAG injects external knowledge at query time, so it suits private documents, FAQs, and frequently changing data. Fine tuning changes a model's behavior, style, or domain skill by training it on examples, so it suits fixed formats, specialized tone, or domain-specific tasks. Start with RAG and good prompting, and move to fine tuning only when prompts and retrieval can't reach the quality you need — many production systems combine both.

LoRA (Low-Rank Adaptation) is a parameter-efficient fine tuning technique where, instead of updating all the weights of a large model, you train small low-rank matrices while the base weights stay frozen. This slashes GPU memory and cost. QLoRA goes further by quantizing the base model to 4-bit, letting you fine tune billion-parameter LLMs on a single consumer GPU — which is why LoRA and QLoRA are the default choices for most practical LLM fine tuning today.

In practice: define one narrow task, prepare a clean instruction dataset (a few hundred to a few thousand good examples), pick a base model, and use PEFT with LoRA or QLoRA in PyTorch or the Hugging Face ecosystem. Train on a rented or free GPU, evaluate on a held-out set, and watch for overfitting and catastrophic forgetting by tuning learning rate and epochs. Do your first LLM fine tuning project on a small model using Colab or Kaggle before attempting larger runs.

The main LLM fine tuning techniques are full fine tuning (updating all weights — powerful but expensive), parameter-efficient methods like LoRA and QLoRA that train small adapter layers instead, and lighter approaches such as prompt tuning and adapters. For most teams, LoRA or QLoRA on a 7B–13B model offers the best balance of cost and quality, while full fine tuning is reserved for cases with large datasets and budgets.

Expect questions like: when would you fine tune versus use RAG or prompt engineering; LoRA vs QLoRA vs full fine tuning; how you prepare and clean training data; how you prevent overfitting and catastrophic forgetting; how you evaluate a fine-tuned model; and how you'd deploy and serve it cost-effectively. Interviewers increasingly ask you to walk through a real LLM fine tuning project, so hands-on experience matters more than memorized definitions — and practicing with 1:1 mock interviews is a common way candidates prepare.

Yes — demand has grown fast across IT services, global capability centers, AI startups, and product companies. Common roles include GenAI Engineer, AI Engineer, ML Engineer, and LLM Engineer, with LLM fine tuning, RAG, agents, and MLOps as core day-to-day skills. The strongest route in is Python plus PyTorch, hands-on LLM projects, and deployment experience with FastAPI and cloud platforms; many people transition into these jobs from data engineering or software backgrounds.

It depends on how you use it. Hosted API models charge per token — light usage can cost a few hundred rupees a month, while heavy production traffic on large models can run into lakhs; open-source models are free to download but you pay for GPU hosting and fine tuning. Add the cost of embeddings and a vector database if you're building RAG. For learning, free tiers and small open models on Colab keep the cost close to zero.