02. Autoregressive Generation: Tokenization (BPE, WordPiece, SentencePiece), Sampling (Temperature, Top-K, Top-P) • Generative AI, Large Language Models (LLMs) & Vector RAG • Rishabh Ki Kaksha

Generative AI, Large Language Models (LLMs) & Vector RAG › 02. Autoregressive Generation: Tokenization (BPE, WordPiece, SentencePiece), Sampling (Temperature, Top-K, Top-P)

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

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# 02. Autoregressive Generation: Tokenization (BPE, WordPiece, SentencePiece), Sampling (Temperature, Top-K, Top-P)

> Generative AI & Vector RAG Notes by Rishabh - Topic 02

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### 1. Tokenization Engines: Byte-Pair Encoding (BPE) 🔤

Characters $\rightarrow$ Subwords $\rightarrow$ Tokens.  
**BPE Algorithm:** Most frequent adjacent byte pairs ko recursively merge karta hai (e.g. OpenAI `tiktoken` with ~100k vocabulary).

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### 2. Decoding Strategies & Sampling Parameters 🎛️

$$P(x_i) = \frac{\exp(z_i / T)}{\sum_j \exp(z_j / T)}$$

1. **Temperature ($T$):**
   * $T \rightarrow 0$ (Greedy Search): Deterministic, picks highest probability token (Best for Code/Math).
   * $T \approx 0.7 - 1.0$: Creative & natural flow (Best for Story writing / Chat).
2. **Top-K Sampling:** Sirf top $K$ highest probability tokens me se sample karta hai (e.g. $K=50$).
3. **Top-P (Nucleus Sampling):** Smallest set of tokens jinka cumulative probability sum $\ge P$ (e.g. $P=0.9$) ho, sirf unhi me se sample karta hai!

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### 3. Master Comparison Matrix

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

1. **Greedy Decoding vs Beam Search me kya difference hai?** -> Greedy search har step par local best token pick karta hai; Beam Search top $B$ candidate sentence hypotheses ko parallel track karke global probability maximize karta hai.
2. **Speculative Decoding LLM inference speed 2x kaise karti hai?** -> Ek chhota fast Draft Model (e.g. 1B) tokens speculate karta hai aur bada Target Model (e.g. 70B) unhe single forward pass me parallel verify karta hai.
3. **Byte-level BPE Out-Of-Vocabulary (OOV) tokens ko 100% eliminate kaise karta hai?** -> Raw bytes (0 to 255) fallback hone ki wajah se kisi bhi novel word ya emoji ko byte sequence me encode kiya ja sakta hai.

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**Next Topic:** 03. Prompt Engineering Architecture: Few-Shot Prompting, Chain-of-Thought (CoT), ReAct & System Prompt Hardening

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