Retrieval Augmented Generation (RAG) and Knowledge Integration

This topic focuses on making RAG outputs traceable and auditable. It explains mechanisms for attaching metadata, such as document IDs, timestamps, and access control tags, to retrieved chunks and ensuring that the LLM includes or reflects this metadata in its responses. Learners design prompts that require the model to output...

This topic focuses on making RAG outputs traceable and auditable. It explains mechanisms for attaching metadata, such as document IDs, timestamps, and access control tags, to retrieved chunks and ensuring that the LLM includes or reflects this metadata in its responses. Learners design prompts that require the model to output citations next to claims or at the end of the answer, and understand limitations such as token budget and the risk of citation hallucination. The topic discusses regulatory and compliance contexts, such as financial advice, healthcare, and legal services, where traceability to authoritative sources is mandatory. It addresses UX patterns for showing citations and enabling users to inspect the underlying documents. The topic also covers logging and audit requirements and how to maintain a chain-of-custody for data as it flows from retrieval through generation.

This topic introduces RAG as an architecture where a retriever component selects relevant documents from an external knowledge store and an LLM generator uses those documents to craft responses. It explains the core loop: a user query is embedded or otherwise processed, relevant chunks of documents are retrieved via similarity...

This topic introduces RAG as an architecture where a retriever component selects relevant documents from an external knowledge store and an LLM generator uses those documents to craft responses. It explains the core loop: a user query is embedded or otherwise processed, relevant chunks of documents are retrieved via similarity search, and these chunks are then passed with the query into an LLM prompt. The LLM is instructed to ground its answer only on the retrieved context, often including explicit directions to quote or cite documents. The topic highlights advantages of RAG, including the ability to answer questions about proprietary or frequently changing data without retraining the model, reducing hallucinations by constraining the model to retrieved evidence, and supporting compliance by making data sources explicit. It contrasts RAG with pure prompting, which relies solely on model pre-training, and with fine-tuning, which requires changing model weights. It also introduces limitations of RAG, such as retrieval errors, context window limits, and the need for robust chunking and indexing strategies.

This topic reviews classical information retrieval concepts and how they underpin modern RAG systems. It explains how documents are tokenized, normalized, and stored in inverted indexes for keyword-based retrieval, and how ranking functions such as TF-IDF and BM25 compute relevance scores. It then introduces neural and dense retrieval methods, where...

This topic reviews classical information retrieval concepts and how they underpin modern RAG systems. It explains how documents are tokenized, normalized, and stored in inverted indexes for keyword-based retrieval, and how ranking functions such as TF-IDF and BM25 compute relevance scores. It then introduces neural and dense retrieval methods, where both queries and documents are mapped into a shared vector space using neural encoders, enabling similarity search via cosine or dot-product distance. The topic compares sparse and dense retrieval regarding recall, precision, latency, and hardware requirements. It also describes indexing pipelines, including document cleaning, chunking, metadata tagging, and index building. Learners see how retrieval quality directly affects LLM output because the model can only ground answers in whatever context is retrieved, making retrieval design a central concern for prompt engineers working with RAG.

This topic focuses on embedding-based retrieval and the database infrastructure required for large-scale similarity search. It explains how embedding models map words, sentences, or documents into high-dimensional vectors where semantic similarity corresponds to geometric closeness. It then introduces vector databases such as FAISS, Pinecone, Chroma, Milvus, and Weaviate, which are...

This topic focuses on embedding-based retrieval and the database infrastructure required for large-scale similarity search. It explains how embedding models map words, sentences, or documents into high-dimensional vectors where semantic similarity corresponds to geometric closeness. It then introduces vector databases such as FAISS, Pinecone, Chroma, Milvus, and Weaviate, which are optimized to store and query billions of vectors efficiently. The topic discusses index types (IVF, HNSW, PQ), approximate nearest neighbor search, latency-accuracy tradeoffs, and metadata filtering. Learners examine design decisions such as embedding granularity (sentence vs. paragraph vs. document), dimensionality, and update strategies as documents change. The topic also connects embeddings to downstream prompting, showing how retrieved embeddings are turned back into text chunks and inserted into prompts as grounding context. Finally, it notes practical issues like cost, vendor lock-in, and data residency that influence vector database choice.

This topic connects retrieval pipelines to prompt design. Learners design RAG prompts that clearly separate the user query, the retrieved context, and system-level instructions. Typical templates tell the model to answer only using the provided context, to avoid fabricating information, to cite passages or documents when answering, and to state...

This topic connects retrieval pipelines to prompt design. Learners design RAG prompts that clearly separate the user query, the retrieved context, and system-level instructions. Typical templates tell the model to answer only using the provided context, to avoid fabricating information, to cite passages or documents when answering, and to state explicitly when the answer cannot be derived from the context. The topic covers strategies to handle multiple retrieved chunks, such as summarizing them first, grouping by source, or asking the model to compare and contrast conflicting evidence. It also explains how to handle long contexts that approach token limits, including context ranking, chunk summarization, and dynamic context selection. The topic highlights that RAG prompts must balance strict grounding instructions with flexibility to interpret context, and shows how careless prompt design can reintroduce hallucinations or misrepresent retrieved evidence.

This topic examines how to evaluate whether a RAG system is truly improving outcomes. It distinguishes retrieval errors (irrelevant or missing documents) from generation errors (misinterpretation of context by the LLM) and shows how each can be isolated and measured. Learners design evaluation sets that include queries with known correct...

This topic examines how to evaluate whether a RAG system is truly improving outcomes. It distinguishes retrieval errors (irrelevant or missing documents) from generation errors (misinterpretation of context by the LLM) and shows how each can be isolated and measured. Learners design evaluation sets that include queries with known correct answers and measure recall, precision, and factual correctness both with and without retrieval. The topic discusses common RAG failure cases, such as outdated documents in the index, incomplete coverage of edge cases, and overly long contexts that the model cannot fully attend to. It also explains that RAG is not a silver bullet: if required knowledge does not exist in the corpus, RAG cannot magically create it. Learners gain realistic intuition about when RAG is necessary, when it is beneficial, and when simpler prompting or fine-tuning may suffice.