AI & GenAI Courses in Pune — Generative AI, LLMs, RAG and Agentic AI

Which AI & GenAI courses does Archer Infotech offer?

Two tracks that build on each other: Generative AI teaches you to build applications on Large Language Models, and Agentic AI teaches those applications to use tools, hold state and complete multi-step tasks.

Generative AI goes well past prompt engineering. It covers LLM and transformer fundamentals, tokens and context windows, prompt and context engineering, structured outputs, LLM APIs, embeddings, vector databases, semantic search, RAG and advanced RAG, evaluation, fine-tuning concepts, multimodal AI and production GenAI practice.

Agentic AI adds the ability to act: agent fundamentals, tool and function calling, reasoning and planning patterns, agent state and memory, agentic RAG, LangGraph and agent frameworks, human-in-the-loop workflows, multi-agent systems, orchestration, agent evaluation, guardrails, AI security, observability and production deployment.

Alongside these sit shorter focused courses — ChatGPT and LLMs, Prompt Engineering, and AI Tools — for learners who want a specific capability rather than the full engineering track. ChatGPT and LLMs moves from model basics to OpenAI APIs, function calling, RAG, Assistants and a deployed capstone. Prompt Engineering moves from prompt anatomy to examples, structured outputs, evaluation and a domain prompt suite. AI Tools moves from daily-driver text tools to research, creative work, coding assistance, automation and a role-specific workflow.

What is the difference between Generative AI and Agentic AI?

Generative AI creates or transforms content: user → LLM → response. Agentic AI acts on a goal: user goal → agent → reason → choose tool → act → observe → decide → complete the task.

Generative AI is the intelligence layer. It produces chatbots, document question-answering, content and code generation, enterprise search, RAG applications, knowledge assistants and multimodal applications.

Agentic AI is what lets that intelligence interact with systems and do work — research agents, coding agents, customer-support agents, data-analysis agents, workflow automation, enterprise copilots, multi-agent applications and autonomous task execution.

The practical consequence is ordering: an agent that cannot be prompted reliably, cannot retrieve grounded context and cannot be evaluated is an agent that fails unpredictably. Generative AI comes first for that reason, not as a formality.

How do AI, Machine Learning, Deep Learning and Generative AI relate?

They are nested, not competing: Artificial Intelligence is the broad field, Machine Learning is systems that learn patterns from data, Deep Learning is multi-layer neural networks, and Generative AI is the subset of deep learning that creates new content.

Getting this hierarchy right matters because it tells you what you can safely skip. You do not need to train models from scratch to build an LLM application — but you do need to understand what a model is doing when it fails, which is why the foundations are taught rather than assumed.

What is the recommended learning sequence for AI and GenAI?

AI fundamentals → deep learning basics → transformers and LLMs → Generative AI → prompt and context engineering → embeddings and vector databases → RAG → advanced RAG → evaluation → AI agents → tool calling → agent state and memory → Agentic AI → multi-agent systems → guardrails → production AI engineering.

Beyond Agentic AI the field keeps going, and the roadmap below maps where it leads: core agent design, context and tool engineering, agent memory and long-horizon work, self-improving agents, multi-agent orchestration, harness and runtime engineering, sandboxing, and the reliability disciplines — evaluation, observability, reliability and security — that decide whether an agent survives contact with production.

Experienced programmers do not have to walk every step. Foundational topics can be compressed into accelerated prerequisite modules before entering Generative AI, which is how most working developers take this track.

What will you build on the AI & GenAI track?

The focus is application engineering, so every stage produces something that runs — from a first LLM-powered app to a multi-agent system with evaluation and monitoring.

• LLM-powered applications — foundation models integrated into Python and web apps
• Intelligent document assistants — upload documents and question them with RAG
• Semantic search systems — retrieval by meaning rather than keywords
• Enterprise RAG applications — LLMs grounded in private organisational data
• AI research assistants — search, retrieve, analyse and synthesise
• Tool-using AI agents — agents that call APIs, databases and software tools
• Stateful AI workflows — context maintained across long-running processes
• Multi-agent systems — specialised agents collaborating on complex tasks
• Production AI applications — security, evaluation, monitoring, tracing and deployment

What technologies does the AI & GenAI track cover?

Python and APIs at the base, then foundation models, retrieval, agents, frameworks, evaluation and the production engineering that surrounds them.

The emphasis is architecture and engineering concepts first, implementation second. Frameworks change every few months; understanding why a retrieval step exists does not.

• Programming and APIs: Python, REST, FastAPI, structured data
• Foundation models: commercial and open-source LLM concepts
• AI application development: prompting, structured outputs, tool calling, streaming
• Knowledge and retrieval: embeddings, vector databases, semantic search, RAG
• Agentic systems: agents, tools, state, memory, planning, orchestration
• Frameworks: modern LLM and agent-development frameworks
• Evaluation: golden datasets, LLM and RAG evaluation, AI quality testing
• Production engineering: Docker, APIs, observability, tracing, caching, security

Do you need Machine Learning before Generative AI?

No — not a full Data Science or Machine Learning programme. Experienced developers can follow an accelerated path: programming → AI/ML fundamentals → deep learning basics → transformers and LLMs → Generative AI → Agentic AI.

A working understanding of AI, machine learning, neural networks and transformers genuinely helps, because it is what lets you reason about a model's failure rather than guess at it. But that understanding can be built in an accelerated foundation module rather than a full ML course.

Learners who do want depth in model training, statistics and algorithms should start with the Data & AI courses instead, then come back to this track.

Who should learn AI & GenAI?

Software and Python developers, Java and .NET engineers moving into AI, backend and full-stack developers, data scientists and ML engineers, cloud and DevOps professionals, engineering students, and technical leads planning an AI transition.

This is an engineering track, not an overview. It suits people who are comfortable writing and debugging code, because everything after the first module is built rather than watched.

How practical is the AI training?

AI cannot be learned from slides or prompt demonstrations — the track is built around coding exercises, working APIs and projects you deploy.

The goal is that you learn not only how to use an AI model, but how to design the complete system around it — which is the difference the market pays for.

• Coding exercises against real AI APIs
• Prompt and context experiments with measured outcomes
• RAG applications and vector search implementations
• Agent workflows and tool integrations
• Mini projects plus a capstone
• GitHub portfolio development
• Architecture discussions and design reviews
• Mock interviews and interview preparation