AI & LLM Systems
RAG & Vector Databases
Retrieval-Augmented Generation: semantic embeddings, vector indices, and context synthesis.
Retrieval-Augmented Generation (RAG) grounds LLMs on external authoritative knowledge without expensive fine-tuning. By chunking documents, calculating dense semantic embeddings, indexing with HNSW or IVF, and retrieving relevant passages at runtime, RAG prevents hallucinations and provides cited answers.
Virtual Lab Ready
Automated testing sandbox running in your browser.
Launch First Lab ⚡No local installation required
Hands-on Practice
Interactive Terminal Labs
Real environment tasks with automated validation. Write real commands, verify state, and debug errors.
Build a Vector Index Pipeline Config
Topic: Embeddings and Vector Search
Configure a document chunking and vector index pipeline specification with chunk size and overlap parameters.
Key Checks:
- Directory ~/rag-pipeline exists
- File ~/rag-pipeline/indexing-config.yaml exists
Split a Document into Chunks
Topic: Chunking Documents
Retrieval pipelines split documents into chunk files before embedding them. Create a chunks directory holding two one-line chunks, then write a manifest.txt that lists both filenames.
Key Checks:
- Directory ~/rag-lab/chunks exists
- Files chunk-01.txt and chunk-02.txt exist
Configure the Vector Store
Topic: The Vector Store
The vector store holds the embeddings and answers the nearest-neighbour queries. Write the store configuration - index type, vector dimension and how many replicas it runs.
Key Checks:
- Directory ~/rag exists
- File ~/rag/store.yaml exists
Blend Dense and Sparse Retrieval
Topic: The Vector Store
Dense vectors find meaning, keyword search finds exact identifiers. Blend the two and add a reranker on top - then say how much weight each side gets.
Key Checks:
- File ~/rag/hybrid-search.yaml exists
- The config defines a dense and a sparse retriever
System Design
The RAG Ingestion & Query Pipeline
Understand how this tool operates internally before running production workloads.
Internal Mechanics
During ingestion, documents are parsed, chunked, and embedded into vectors stored in an index. At query time, the user prompt is converted to a query vector, matched against top-k similar chunks, and synthesized by the LLM.
Mental Model Rule of Thumb
Never treat RAG & Vector Databases as a black box. Understanding its boundary with the kernel, filesystem, and network stack prevents 90% of production outages.
Core Architectural Subsystems
Document Parser & Chunker
Splits PDFs, Markdown, and HTML into semantic chunks with overlap.
Embedding Model
Transforms text into high-dimensional vectors (e.g. OpenAI text-embedding-3).
Vector Database (Qdrant/Pinecone/pgvector)
Performs fast approximate nearest neighbor (ANN) search.
Reranker (Cohere/BGE)
Cross-encoder scoring retrieved candidates to place highest quality context first.
Developer Reference
Command Reference & Cheatsheet
Curated commands, flags, and one-liners tested for everyday operational productivity.
python -m pip install qdrant-client sentence-transformersInstall vector DB client and local open-source embedding models
python ingest.py --chunk-size 512 --overlap 50Run ingestion pipeline splitting documents and building index
python query.py --top-k 5 --hybridQuery vector index using combined semantic and keyword retrieval
Fundamental Primitives
Key Terminology & Primitives
Core abstractions you must master to communicate and build with confidence.
Cosine vs Dot Product
Measures of vector proximity; for normalized embeddings, dot product equals cosine similarity and computes faster.
Hybrid Search (Dense + Sparse)
Combines dense vector semantic search with BM25 keyword matching to capture exact technical terms and acronyms.
Lost in the Middle
The tendency of LLMs to attend best to tokens at the very beginning and end of long context prompts, ignoring mid-prompt information.
Context Window Packing
Fitting relevant chunks within the model context limit while preserving room for reasoning and generation.
Production War-Stories
Common Pitfalls & Debugging Playbook
The exact errors that take down production systems and how to diagnose and resolve them.
Hallucinations despite RAG retrieval
Symptom
Model invents answers not found in the provided context documents.
Resolution
Add strict system prompt constraints ('Answer strictly using the provided context. If the answer cannot be found, respond with I do not know'). Use a reranker to filter out irrelevant chunks.
Reliability Standards
Production Readiness Checklist
Review this checklist before deploying workloads to staging or production clusters.
- ✓Evaluate retrieval quality using RAGAS or TruLens (Context Precision, Recall, Faithfulness).
- ✓Implement hybrid search (vector + BM25) for technical documentation with code identifiers.
- ✓Use rerankers before passing retrieved context into expensive frontier model prompts.
- ✓Track latency across every step: embedding calculation, vector query, and model generation.
Full Curriculum Track
Master RAG & Vector Databases as part of the AI Engineer path
Tools do not exist in isolation. Learn how RAG & Vector Databases connects with other systems, CI/CD pipelines, and infrastructure patterns.