16. RAG Evaluation Framework (RAGAS): Faithfulness, Answer Relevance, Context Precision & Context Recall • Generative AI, Large Language Models (LLMs) & Vector RAG • Rishabh Ki Kaksha

Generative AI, Large Language Models (LLMs) & Vector RAG › 16. RAG Evaluation Framework (RAGAS): Faithfulness, Answer Relevance, Context Precision & Context Recall

45 mins

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Course Syllabus

Module 1: Large Language Model (LLM) Fundamentals & Transformer Architecture

01. Transformer Architecture Deep Dive: Self-Attention Mechanism, Multi-Head Attention & Positional Encoding 02. Autoregressive Generation: Tokenization (BPE, WordPiece, SentencePiece), Sampling (Temperature, Top-K, Top-P) 03. Prompt Engineering Architecture: Few-Shot Prompting, Chain-of-Thought (CoT), ReAct & System Prompt Hardening

Part 1: LLM Internals & Vector RAG Pipelines

Module 2: Embeddings, Vector Spaces & Similarity Mathematics

04. Vector Embeddings: Dense vs Sparse Vectors (OpenAI text-embedding-3, BGE-Large, BM25) 05. Similarity Metrics: Cosine Similarity, Dot Product, Euclidean Distance & Manifold Geometry 06. Approximate Nearest Neighbor (ANN) Algorithms: HNSW (Hierarchical Navigable Small World) & IVF-PQ Indexes

Module 3: Vector Databases: Pinecone, Qdrant, Milvus, Chroma & pgvector

07. Vector Database Architecture: Comparing Pinecone, Qdrant, Weaviate, Milvus & PostgreSQL with pgvector 08. Metadata Filtering & Payload Indexing for High-Performance Multitenant Vector Search 09. Embedded Vector Stores: ChromaDB, DuckDB & In-Memory FAISS (Facebook AI Similarity Search)

Module 4: Advanced RAG Architecture: Chunking, Reranking & Hybrid Search

10. Document Chunking Strategies: Fixed Size, Recursive Character, Semantic Chunking & Parent Document Retrievers 11. Hybrid Search Architecture: Combining Keyword Search (BM25) + Dense Vector Search with Reciprocal Rank Fusion (RRF) 12. Cross-Encoder Reranking: Integrating Cohere Rerank / BGE-Reranker for Pinpoint Context Relevance

Module 5: Context Window Optimization, Multi-Query & Graph RAG

13. Multi-Query Generation, Sub-Question Decomposition & Step-Back Prompting for Complex Questions 14. Context Compression & Lost-in-the-Middle Mitigation Strategies 15. Graph RAG Architecture: Combining Knowledge Graphs (Neo4j / NetworkX) with Vector Search for Relational Insights

Module 6: RAG Evaluation (RAGAS), Guardrails & Production Deployment

16. RAG Evaluation Framework (RAGAS): Faithfulness, Answer Relevance, Context Precision & Context Recall 17. Guardrails & Safety: NeMo Guardrails, Llama Guard & Prompt Injection Defense Mechanisms 18. Streaming Output Architecture with Server-Sent Events (SSE) & WebSocket RAG Responses 19. Caching in GenAI: Semantic Caching with GPTCache & Redis to Slash API Costs by 70% 20. Production GenAI Deployment: Building an Enterprise PDF/Docs AI Assistant with LangChain & LlamaIndex

Interactive Lesson Studio

# 16. RAG Evaluation Framework (RAGAS): Faithfulness, Answer Relevance, Context Precision & Context Recall

> Generative AI & Vector RAG Notes by Rishabh - Topic 16

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### 1. The RAG Triad of Metrics (RAGAS Framework 📊)

1. **Faithfulness (Hallucination Detection):** Generated answer me diye gaye claims retrieved context se kitne supported hain?
   $$\text{Faithfulness} = \frac{\text{Supported Claims}}{\text{Total Claims in Answer}}$$
2. **Answer Relevance:** Generated answer user ki original query ko kitna direct address karta hai?
3. **Context Precision:** Retrieved context me relevant chunks top ranks par hain ya nahi?
4. **Context Recall:** Ground truth answer me zaroori saari information retrieved context me maujood thi ya nahi?

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### 2. Master Comparison Matrix

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### 3. Interview Ke 3 Sawal Is Topic Se

1. **LLM-as-a-Judge evaluation methodology kya hoti hai?** -> Strong judge model (GPT-4 / Claude 3.5 Sonnet) use karke synthetic test cases aur rubrics ke basis par quantitative scores (0-1) generate karna.
2. **Synthetic Test Data Generation (Ragas Testset Generator) kaise kaam karta hai?** -> Knowledge base documents se automatically reasoning, conditioning aur multi-context questions generate karna.
3. **BLEU aur ROUGE metrics RAG evaluation me obsolete kyu hain?** -> Kyunki wo sirf surface n-gram word overlaps check karte hain; semantic meaning aur factual correctness evaluate nahi kar sakte.

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**Next Topic:** 17. Guardrails & Safety: NeMo Guardrails, Llama Guard & Prompt Injection Defense Mechanisms

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