Practical Agentic AI: RAG, Planning & Vector Search
Course Overview
What You'll Learn
- Differentiate LLMs vs. Agentic AI across autonomy, memory, and tool use.
- Apply the Perceive→Reason→Act loop to real tasks.
- Implement MCP (Model–Controller–Prompter) orchestration for agents.
- Ground responses with RAG for factuality and reliability.
- Build a CLI agent that collects preferences and runs a continuous recommendation loop.
- Integrate Tavily search + ChatGPT/Gemini for retrieval and ranking.
- Persist interaction history (SQLite) and migrate to ChromaDB embeddings.
- Engineer topic “pillars,” weighted selection, and semantic re-ranking.
- Generate user-facing explanations for recommendations (XAI).
- Address agent risks: memory poisoning, goal manipulation, identity spoofing.
About This Free Course
This course contains the use of free applied artificial intelligence machine learning quizzes course.
Generative AI is moving from reactive “knowers” to proactive “doers” that perceive, plan, and act toward goals. This shift—Agentic AI—pairs LLM reasoning with tools, memory, and workflows so systems can execute multi-step tasks autonomously.
Enterprises now expect agents that ground answers with RAG, orchestrate APIs, and operate reliably with guardrails—raising new questions about autonomy, accountability, and oversight.
What This Course Covers
You’ll learn an end-to-end Agentic AI stack: the Perceive→Reason→Act loop; Retrieval-Augmented Generation; planning & memory; the MCP (Model–Controller–Prompter) workflow; and framework choices (LangChain, LlamaIndex, CrewAI, AutoGen). We translate concepts into an applied build: a CLI “Personalized News Curator” that uses Tavily for live search, ChatGPT/Gemini for ranking & summaries, an in-memory/SQLite → ChromaDB store, topic-pillar weighting, semantic re-ranking, and explanation generation.
What You Will Learn
Differentiate LLMs vs. Agentic AI across autonomy, memory, and tool use.
Apply the Perceive→Reason→Act loop to real tasks.
Implement MCP (Model–Controller–Prompter) orchestration for agents.
Ground responses with RAG for factuality and reliability.
Build a CLI agent that collects preferences and runs a continuous recommendation loop.
Integrate Tavily search + ChatGPT/Gemini for retrieval and ranking.
Persist interaction history (SQLite) and migrate to ChromaDB embeddings.
Engineer topic “pillars,” weighted selection, and semantic re-ranking.
Generate user-facing explanations for recommendations (XAI).
Address agent risks: memory poisoning, goal manipulation, identity spoofing.
Compare frameworks (LangChain, LlamaIndex, CrewAI, AutoGen) to match goals.
Apply prompt and project structuring best practices for agentic coding.
Real-World Application & Use Cases
We design a proactive companion that monitors interests, fetches fresh articles, updates a preference model from likes/dislikes, and iterates autonomously—illustrating agent planning, tool use, and memory in a compact workflow.
You’ll see how to evolve from a simple loop to a production-style recommender: topic extraction, weighted exploration vs. exploitation, semantic vectors, source allow-listing, recency decay, and a user-readable “why this was recommended” message.
Course Format & Learning Experience
Structured modules combine concept briefings with hands-on labs: set up the environment and MCP scaffolding; implement RAG; wire Tavily+ChatGPT; add persistence (SQLite → ChromaDB); introduce topic pillars & semantic re-ranking; add explanation UX; then harden with tests and risk mitigations. Expect checklists, prompts, and refactors aligned to engineering best practices.
Instructor
Taught by Shreejit Gangadharan with 12 years of industry experience in companies like Flipkart, Microsoft, and Google.
Updated with 2024–2025 practices across MCP orchestration, vector stores, topic-pillar ranking, source allow-listing, recency decay, and agent risk controls.
Primary Topics/Keywords: Agentic AI, Perceive-Reason-Act, MCP workflow, RAG, LangChain, LlamaIndex, CrewAI, AutoGen, Tavily, ChatGPT/Gemini, SQLite, ChromaDB, embeddings, semantic re-ranking, topic pillars, explainability, agent risks.
Prerequisites:
Working knowledge of Python and virtual environments; comfort with CLI & Git.
Understanding of HTTP APIs and JSON; basic familiarity with LLM prompts.
Tools & Frameworks Used: LangChain, Tavily, ChatGPT/Gemini, SQLite, ChromaDB, pytest.
Capstone Project: CLI “Personalized News Curator” with preference learning, topic pillars, and semantic re-ranking, plus user-facing explanations.
Who Should Take This Course
"Practical Agentic AI: RAG, Planning & Vector Search" is aimed at people who want a practical, structured introduction to development without paying full price for it. It's a solid fit if you're starting out in development and want a guided course rather than piecing tutorials together yourself, if you've tried free YouTube content on the topic and want something more organized in around 2.5, or if you already work in a related area and want a refresher you can finish at your own pace. Since enrollment happens on Udemy itself, you keep full access to view the lectures, download any provided resources, and revisit the material later — this isn't a stripped-down or time-limited version of the course.
Why This Course Is Worth Taking
Our take: this listing earns a spot on FreeWebCart because the coupon we verified actually brings the price to $0, not just a token discount, and the course carries a 5/5 rating on Udemy from 2+ students who've already enrolled. That combination — real reviews plus a working 100% OFF code — is what we look for before publishing a development course. It won't replace hands-on experience or a full degree program, but as a low-risk way to test whether development is worth pursuing further, or to pick up one specific skill, the free price tag makes it an easy yes while the coupon lasts.
Pros & Cons
👍 Pros
- 100% free to enroll via this coupon (normally $54.99)
- Lifetime access on Udemy once enrolled, even after the coupon expires
- Rated 5/5 by past students on Udemy
- Self-paced — no fixed schedule or live sessions to attend
👎 Cons
- Coupon is time-limited and can expire before you enroll
- No live instructor support — questions go through Udemy's Q&A, not us
- Certificate is a Udemy completion certificate, not an accredited qualification
Frequently Asked Questions
Is "Practical Agentic AI: RAG, Planning & Vector Search" really free?
Yes — we verified a 100% OFF Udemy coupon for this development course before publishing it. Enroll directly on Udemy using the button below; no credit card is needed while the coupon is active.
How long will this coupon last?
Udemy coupons typically last 1–3 days or expire after roughly 1,000 enrollments, whichever comes first. If the price on Udemy no longer shows $0 when you click through, the coupon has expired since we last checked it.
Do I keep access after the coupon expires?
Yes. Once you enroll while the coupon is live, "Practical Agentic AI: RAG, Planning & Vector Search" is yours to keep on Udemy — including any future updates the instructor makes — even after the coupon runs out.
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