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Home/Courses/Master AI-RAG-MCP

Interactive Lab

RAG Playground

Hands-on interactive environment for the Master AI-RAG-MCP curriculum. Explore retrieval strategies, visualise the Agentic RAG workflow, and evaluate documents with RAGAS — all in-browser.

① RAG Comparator ② Agentic Flow Visualiser ③ RAGAS Eval Lab

Part 1·

Retrieval Strategy Comparator

Configure parameters and compare Dense, Sparse, and Hybrid retrieval strategies side-by-side.

Corpus Text (your knowledge base)

Retrieval Augmented Generation (RAG) is a technique that combines information retrieval with text generation. RAG systems first retrieve relevant documents from a knowledge base, then pass them to a language model as context. This approach grounds the model's responses in factual, up-to-date information. Agentic RAG extends this by giving the AI agent autonomy to decide when and how to retrieve, critique, and re-query. Vector databases store embeddings that represent semantic meaning, enabling similarity search. BM25 is a classic sparse retrieval algorithm based on term frequency and inverse document frequency. Hybrid retrieval combines dense vector search and sparse BM25 using Reciprocal Rank Fusion (RRF). RAGAS is a framework for evaluating RAG pipelines across faithfulness, answer relevancy, and context precision. MCP (Model Context Protocol) is a universal standard for connecting AI models to external data sources. Chunk size determines how text is split; smaller chunks give precision, larger chunks give more context.

Query

Press Enter or click Run Comparison

Retrieval Parameters

Chunk Size (tokens)

Embedding Model

Top-K Results

Strategy Comparison Reference

Strategy	Algorithm	Best For	Weakness	Typical MRR
Dense	ANN / Cosine	Semantic queries	Out-of-vocab terms	0.85–0.94
Sparse	BM25 / TF-IDF	Exact keyword match	Synonym blindness	0.72–0.85
Hybrid	RRF fusion	General purpose RAG	Higher latency	0.88–0.96

Part 2·

Agentic RAG Workflow Visualiser

Watch the Retriever → Critic → Generator pipeline animate in real-time. Click ▾ on any completed node to inspect its thought log and tool calls.

Query

Node Roles

Retriever

Fetches top-K chunks via vector + BM25 search

Critic

Scores relevance; decides re-query or proceed

Generator

Builds prompt from context; calls LLM; returns answer

Retriever

Waiting…

↓

Critic

Waiting…

↓

Generator

Waiting…

Agentic RAG Loop — Architecture Reference

User Query

→

Query Router

→

Vector DB (Dense)

BM25 Index (Sparse)

→

RRF Merger

→

Critic

→

LLM Generator

→

Response

The Critic can trigger a re-query loop (up to N iterations) before passing context to the Generator — this is the key difference from standard single-pass RAG.

Part 3·

RAGAS Eval Lab

Upload a .txt or .pdf file (or paste text). We simulate QA-pair generation and score your RAG pipeline on the three core RAGAS metrics.

Click to upload .txt or .pdf

or paste text directly below

Or Paste Text

Scorecard will appear here

Upload or paste text, then click evaluate

RAGAS Metrics — Course Reference

Faithfulness

Measures hallucination. Low score = model invents facts not present in retrieved context.

Formula: Claims supported by context ÷ Total claims in answer

Range: 0 – 1

Target: ≥ 0.85

Answer Relevancy

Measures on-topic quality. Penalises incomplete or off-topic answers.

Formula: Mean cosine similarity of back-generated questions to original query

Range: 0 – 1

Target: ≥ 0.80

Context Precision

Measures retrieval precision. Low score = too much noise in the retrieved context.

Formula: Relevant chunks in top-K ÷ Total K retrieved

Range: 0 – 1

Target: ≥ 0.75

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