Most Generative AI courses teach you to call an API. That is roughly two hours of material stretched across several weeks. The skills that get people hired are the ones that come after the API call: retrieval that returns the right context, evaluation that catches regressions, and deployment that survives contact with real users and real cost limits.
Short answer: AIDisha's Generative AI training is delivered as part of a 6-month AI Engineer course. It covers LLMs, prompt engineering, embeddings, vector databases, RAG, fine-tuning, LangChain, LangGraph, evaluation with LangSmith, and deployment on AWS Bedrock — grounded in Python, ML and NLP fundamentals taught first. Total fee ₹34,994.
What Generative AI engineering actually involves
A working Generative AI application is mostly not the model. It is the surrounding system: how documents are chunked and embedded, how relevant context is retrieved and ranked, how prompts are constructed and versioned, how outputs are validated, how failures are traced, how latency and token spend are kept inside budget, and how the whole thing is deployed and monitored.
This is why "learn Generative AI in 30 days" offerings produce candidates who interview poorly. They can demonstrate a prototype but cannot explain why retrieval quality dropped when the document set grew, or how they would evaluate one prompt against another. Those are the questions asked in second-round interviews.
What the course covers
Foundations — large language models
How transformers and attention work; tokenisation and why it drives cost; context windows and their limits; temperature, top-p and sampling; the differences between GPT, Claude, Gemini, Llama and Mistral, and how to choose between them for a given task and budget.
Prompt engineering
Zero-shot, few-shot and chain-of-thought prompting; structured output and schema enforcement with Pydantic; system prompt design; guardrails; and prompt versioning — treating prompts as code under review rather than strings pasted into a notebook.
Embeddings and vector databases
Embedding models and how to select one; similarity metrics; chunking strategies and why they determine retrieval quality more than model choice does; and hands-on work across Chroma, FAISS, Pinecone, Qdrant, Milvus and Weaviate, including when a lightweight local store beats a managed service.
Retrieval-Augmented Generation (RAG)
The full pipeline: ingestion, chunking, embedding, indexing, retrieval, reranking and generation. Then the parts most courses skip — hybrid search, query rewriting, evaluating retrieval precision and recall separately from generation quality, and diagnosing the difference between a retrieval failure and a generation failure.
Fine-tuning and model adaptation
When fine-tuning is genuinely the right answer and when RAG or better prompting is cheaper and faster; dataset preparation; parameter-efficient methods including LoRA; and evaluating a fine-tuned model against its base honestly.
Orchestration with LangChain and LangGraph
Chains, tools, memory and structured agents in LangChain; stateful multi-step graphs in LangGraph. Covered in more depth on the Agentic AI course page.
Evaluation, tracing and cost control
LangSmith for tracing and dataset-driven evaluation; building regression suites for prompts; measuring hallucination and faithfulness; caching with Redis; and token accounting — because an LLM feature that works but costs ₹40 per user request will not ship.
Deployment
Serving LLM applications through FastAPI; building interfaces with Streamlit and Gradio; containerising with Docker; deploying on AWS and AWS Bedrock; and monitoring with CloudWatch, Prometheus and Grafana.
Projects
Production RAG system
Document ingestion through to a deployed, evaluated question-answering service over a private corpus.
Fine-tuned domain LLM
Adapt an open model to a specific domain and benchmark it honestly against the base model and against a RAG baseline.
Multi-agent workflow
A LangGraph system where several agents with distinct tools collaborate on a task with shared state.
Deployed LLM service
FastAPI backend, Docker, AWS deployment, CI/CD pipeline, monitoring and cost tracking.
Prerequisites
None. The programme starts with Python from first principles and moves through ML and NLP before Generative AI begins in month 3. That sequencing is the point — Generative AI taught without those foundations produces people who can copy examples but cannot debug them.
Frequently asked questions
What is a Generative AI course and what does it teach?
A Generative AI course teaches you to build applications on top of large language models rather than train models from scratch. Core topics are prompt engineering, embeddings, vector databases, retrieval-augmented generation (RAG), fine-tuning, evaluation, and deploying LLM applications to production. AIDisha covers all of these across months 3 to 6 of its AI Engineer programme.
Do I need machine learning knowledge before learning Generative AI?
You do not need it to start, but you need it to be good. Working with LLMs without understanding embeddings, tokenisation, attention or evaluation metrics means you cannot debug why a system fails. AIDisha teaches ML and NLP fundamentals first, then builds Generative AI on that base — which is why the programme runs six months rather than six weeks.
Which Generative AI tools does the course cover?
OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral and Ollama for local models; LangChain and LangGraph for orchestration; Chroma, FAISS, Pinecone, Qdrant, Milvus and Weaviate for vector storage; Hugging Face for open models; LangSmith for tracing and evaluation; AWS Bedrock for managed deployment; and Streamlit and Gradio for interfaces.
Is Generative AI a good career choice in 2026?
It is currently the strongest area of AI hiring in India. Generative AI engineer roles are being created faster than trained candidates are entering the market, particularly in Pune, Bengaluru and Hyderabad. The differentiator in hiring is production experience — candidates who have deployed, evaluated and cost-optimised an LLM system rather than only prototyped one.
Can I learn Generative AI without a coding background?
Yes, if the course starts from programming fundamentals. AIDisha's programme opens with Python from scratch — 500+ coding exercises — before any AI content. Non-IT graduates make up a large share of each batch.