Generative AI Course in Lucknow ? LLMs, RAG, AI Agents & More

GenAI Projects You Will Ship

• Private-Knowledge RAG Assistant ? PDF/website Q&A with citations and streaming UI.
• Customer Support Agent ? tool-calling agent that checks orders, refunds policy and escalates correctly.
• Content Pipeline ? generates drafts, rewrites in brand voice and schedules publishing.
• Meeting Notes Analyzer ? transcribes, summarises and extracts action items.
• AI Sales Coach ? evaluates sales calls against a rubric with scores and tips.

Inside a Real RAG Implementation

RAG looks simple in tutorials ? embed documents, query a vector DB, feed results to the LLM. Production reality: your chunks split sentences badly, retrieval returns irrelevant pages, and the LLM hallucinates confidently. This course walks you through every production concern ? chunk sizing, metadata filtering, hybrid search, reranking, citation formatting and evaluation ? using the same debugging workflows professional teams use.

By module 5 you will be able to look at a failing RAG pipeline and say exactly why it fails: retrieval quality, context overflow or prompt design. That diagnostic ability is what clients and employers pay premiums for.

Agents Explained ? From ReAct to Multi-Agent

An AI agent is an LLM loop: think ? act with tools ? observe ? repeat. Simple agents (search + answer) are easy; reliable ones are hard ? they must handle failures, avoid loops and stay within guardrails. You will build a single-agent assistant first, then a two-agent pipeline (researcher + writer), learning where multi-agent systems genuinely add value versus where they just multiply costs.

GenAI Course Fees & Paths

3-day money-back guarantee on all plans; EMI available above ?11,999.

Fine-tuning vs RAG ? Choosing Correctly

• RAG: private knowledge that changes often ? docs, policies, product data. Cheaper and updateable.
• Fine-tuning: teaching a style or format on a stable dataset. Costs more, needs data discipline.
• Rule of thumb: try prompt ? RAG ? fine-tune, in that order. We practise all three.

GenAI Course ? More Questions Answered

GenAI Rapid-Fire Q&A ? 10 Questions From Real Classes

From Course to First Paying Client ? The 30-Day Sprint

Week 1: pick a niche you know (clinics, coaching, real estate, e-commerce). Week 2: build a small RAG or automation demo for that niche using course templates. Week 3: record a 60-second demo and publish on LinkedIn/WhatsApp status. Week 4: offer three businesses a free audit with a clear paid next step. This exact sprint has produced first gigs for alumni within 30 days of the course ? the agency module turns it into your permanent acquisition loop.

Common Client Questions You Will Learn to Answer

• Can the bot use our private data safely? ? yes, via RAG with access control.
• What does it cost monthly? ? API fees from a few hundred rupees for most bots.
• How do we know answers are correct? ? citations and eval dashboards.
• How long to build? ? scoped MVPs in 1?3 weeks, production in 4?8.
• Can we host it ourselves? ? yes, deployment module covers self-hosting.

GenAI Glossary ? 12 Terms You Must Know

• LLM: large language model ? GPT, Claude, Gemini and open models.
• Token: a text unit models process ? about ? of an English word.
• Context window: how many tokens a model can see at once.
• Embedding: a number list representing meaning ? the retrieval currency.
• Vector database: storage tuned for similarity search ? Pinecone, Chroma, FAISS.
• RAG: retrieval-augmented generation ? grounding answers in your documents.
• Chunking: splitting documents into retrievable pieces ? quality depends on it.
• Tool calling: letting the model invoke functions and APIs.
• Agent: an LLM loop that thinks, acts with tools and observes.
• Guardrails: rules keeping outputs safe and on-topic.
• Hallucination: confident, incorrect generation ? controlled, not eliminated.
• Fine-tuning: further training a model on your own data.

GenAI for Existing Businesses ? Where Clients Pay

• Customer support bots with order/status tools ? highest demand.
• Document Q&A for legal, medical and academic teams.
• Content pipelines for marketing and social media teams.
• Meeting transcription and action-item extraction.
• Internal knowledge assistants for onboarding and policies.

Staying Current in a Fast-Moving Field

GenAI changes weekly, but the fundamentals compound: token behaviour, retrieval quality, evaluation and deployment patterns shift slowly even as model names change. The course teaches the fundamentals plus a monitoring habit ? which models to watch, how to read release notes and when to switch ? so you remain current long after graduation without chasing every headline.

GenAI Course ? Last Round of Questions

Generative AI Production Patterns ? RAG, Fine-Tuning, and Enterprise Deployment

Generative AI in production is fundamentally different from calling an API in a notebook. The DSWallah Gen AI course in Lucknow teaches enterprise-grade patterns that companies actually use. Retrieval-Augmented Generation (RAG) solves the hallucination problem by grounding LLM responses in your own documents ? you build a vector database from company knowledge bases, implement semantic search to find relevant chunks, and construct prompts that combine retrieved context with user queries. Fine-tuning adapts pre-trained models to domain-specific tasks ? LoRA (Low-Rank Adaptation) and QLoRA enable fine-tuning on consumer GPUs by updating only a small fraction of parameters, while full fine-tuning is reserved for when you need maximum performance on specialized datasets. Prompt engineering is the fastest path to value ? few-shot prompting, chain-of-thought reasoning, and structured output formats (JSON mode, function calling) get you 80% of the way without any training. Evaluation frameworks measure output quality with metrics like faithfulness, relevance, coherence, and toxicity. The course capstone is building a complete RAG application with document ingestion, vector storage, retrieval pipeline, LLM integration, and a Streamlit interface ? deployable as a portfolio project that demonstrates production Gen AI skills to employers.