Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)

Introduces Large Language Models (LLMs), their core concepts, and the course structure. Covers prerequisites, environment setup, and defines Retrieval-Augmented Generation (RAG).

Explore the four core capabilities of LLMs: generation, summarization, classification, and reasoning. Real-world applications and the importance of RAG for building trust.

Learn to build a stateful chatbot using an LLM. Covers managing conversation history, using system prompts to define behavior, and understanding message roles (system, user, assistant).

Defines prompt engineering and its components. Explains how to control LLM outputs using inference parameters like temperature, top-p, max tokens, and stop sequences.

Apply prompting techniques hands-on. Implement Chain of Thought (CoT) prompting to improve reasoning and test how different inference parameters change model behavior.

Explains the theory of systematically refining prompt instructions by modifying components like Role, Task, Context, Examples, and Output Format.

Provides hands-on practice iteratively refining a prompt to transform a generic recipe analyzer into a precise dietary consultant that produces structured JSON.

Covers the foundations of NLP for LLMs. Defines tokenization, embeddings as semantic vectors, and vector search (similarity search) as the basis for finding relevant information.

Hands-on practice with tokenization. Implementing embedding generation and vector search to build a semantic search system from scratch.

Learn the business trade-offs of model selection. Covers performance, cost, speed, and control (TCO). Compares general-purpose (generation) vs. specialized (reasoning) models.

Apply model selection theory. Calculate Total Cost of Ownership (TCO) including error costs. Implement a hybrid model routing system to balance cost and quality.

Introduces the RAG architecture. Compares naive vs. advanced modular RAG. Covers the data ingestion pipeline, focusing on data formats and intelligent chunking strategies.

Explains semantic search and the role of vector databases. Covers indexing algorithms (HNSW) for speed and advanced retrieval techniques like HyDE and re-ranking.

Learn to write prompts for RAG. Covers grounding answers in context, handling conflicts, managing uncertainty, and enforcing verifiability by generating inline citations.

Build a complete RAG system. Practice vector database operations in ChromaDB, including adding documents, applying metadata filters, and implementing a retrieval and generation pipeline.

Learn to evaluate RAG system quality. Introduces key metrics: Context Precision, Context Recall, Faithfulness, and Answer Relevancy. Covers frameworks like RAGAS.

Implement a RAG evaluation pipeline using the RAGAS framework. Learn to calculate and interpret quality metrics to diagnose and improve your RAG system's performance.