AI LAB
Myntra Conversational Shopping Assistant with RAG
Build a project on a conversational shopping assistant that grounds product recommendations in a real catalog with RAG
Verified Certificate on Successful Completion
What is in it for you?
LLM tokens included
A metered token budget is bundled in, call real models from your code with no API key or extra cost.
AI mentor on tap
Stuck? An in-IDE AI mentor gives hints and debugging help, without handing you the answer.
Instant grading
Submit and get an objective, rubric-based verdict in seconds, pass, or actionable feedback.
Verified certificate
Pass and earn a shareable, verifiable certificate you can add to your LinkedIn profile.
Zero-setup cloud IDE
A ready sandbox with the libraries pre-installed, start building in the browser instantly.
Retry until you pass
Iterate as many times as your budget allows; a fail keeps the lab open to try again.
Grab your slot before the offer expires
Start solving today!
Basic Info
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Project Statement
Business Problem
E-commerce platforms handle millions of shopper queries a month, and generic LLM assistants confidently invent products, prices, and features that aren't in the catalog. A single hallucinated recommendation erodes trust, and even a 5% error rate across 2 million monthly queries means 100,000 misled shoppers and a measurable dent in conversion and CSAT.
AI Solution
You'll build a RAG pipeline that ingests the live product catalog into a vector database and retrieves only real, relevant products for each shopper request. The assistant grounds every recommendation in retrieved catalog items, ranks them against the stated need and budget, and refuses to push off-target products when nothing genuinely fits.
Potential Impact
A grounded assistant can cut hallucinated recommendations to near zero and lift shopper trust and conversion on assisted queries. Teams see faster query resolution, reduced returns from mismatched buys, and CSAT gains of 10-15 points on AI-assisted shopping journeys.
Skills that you will build
Solve a real AI-engineering problem that builds in-demand skills and a portfolio-ready, verified certificate.
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Chunking catalog data for vector ingestionIngesting documents into a vector databaseSemantic retrieval with vector searchBuilding grounded RAG pipelinesPreventing LLM hallucination via retrieval groundingRanking and justifying recommendations against constraintsDesigning honest 'no match' fallbacksPrompt engineering for grounded generationAI Lab Pre-requisites
- Comfortable writing Python functions
- Basic understanding of how LLMs generate text
- Familiarity with the idea of embeddings or vector search (helpful, not required)
Target Roles
- AI Engineer
- LLM Application Developer
- RAG Systems Engineer
- AI Solutions Architect
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Frequently asked questions
Are LLM tokens included?
Yes. The lab runs on a metered LLM budget provided in the environment — you don't need your own API key.
What do I earn on completion?
A verified, shareable credential proving you can build a grounded RAG shopping assistant.
Do I need prior RAG experience?
No. If you can write Python functions and understand the basics of LLMs, you'll be able to complete this lab. The vector client is provided for you.
What makes this different from just prompting an LLM?
You'll learn why direct prompting hallucinates non-existent products, and build a retrieval-grounded pipeline that only recommends items that actually exist in the catalog.
Do I need to set up my own vector database?
No. A ready-to-use vector client with ingest and search methods is provided — you focus on the chunking, retrieval, and grounding logic.
How long does this lab take?
Most learners complete it in around 90 minutes, depending on their familiarity with RAG concepts.