Retrieval-Augmented Generation Systems
Design and ship RAG pipelines that are accurate, observable, and cheap to operate at scale.
Duration
6 weeks · 6-8 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Retrieval-Augmented Generation is the technique of answering from your own documents by chunking them, embedding them into a vector index, retrieving the best passages, and generating a cited answer. It matters now because organizations need assistants that answer from current internal knowledge rather than from a model's frozen training data.
It is used for document Q&A, support assistants, and search over policies or manuals, solving stale answers and invented facts by grounding responses in retrieved passages. It does not solve missing source data: RAG cannot answer what is not in the corpus, it does not fix bad chunking or stale indexes, and citations do not help if the underlying documents are wrong.
By the end the student will be able to build a document ingestion pipeline with chunking and embeddings, a vector search service with hybrid retrieval, reranking, and citation-aware answers, and a RAG evaluation suite measuring retrieval hit rate and answer faithfulness.
Why this course exists
The gap is between a toy demo over ten documents and a pipeline that stays accurate, observable, and cheap as the corpus grows and goes stale. The course teaches the arc from Context to Retrieval to grounded generation to Evaluation to index operations, so students operate RAG that holds up at scale.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST APIs
- Basic understanding of large language models
- Familiarity with databases and APIs
Technologies & tools
Skills you'll gain
A 6 weeks arc, module by module.
- Module 01
Week 1 — When RAG is the right answer
- Module 02
Week 2 — Chunking, embeddings, and indexes
- Module 03
Week 3 — Hybrid search and re-ranking
- Module 04
Week 4 — Evaluating retrieval and generation
- Module 05
Week 5 — Operating vector stores in production
- Module 06
Week 6 — Capstone: a measurable RAG system
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Choose chunking, embedding, and indexing strategies for your corpus.
- Diagnose retrieval failures using recall, precision, and groundedness metrics.
- Implement hybrid search, re-ranking, and query rewriting.
- Operate vector databases with sensible cost and freshness controls.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Document ingestion pipeline
- Project 02
Semantic search service
- Project 03
Grounded Q&A assistant
- Project 04
Citation-aware RAG app
- Capstone
Production RAG system with evaluation
Speak the language first.
- Document chunking
- Chunking splits documents into passages sized for embedding so retrieval returns focused, relevant context.
- Embedding pipelines
- Embedding pipelines convert chunks into vectors that capture meaning for similarity search.
- Vector databases
- Vector databases store embeddings and return the nearest passages for a query at scale.
- Hybrid retrieval
- Hybrid retrieval combines keyword and vector search so exact terms and meaning both count.
- Reranking
- Reranking re-scores top candidates with a stronger model to put the best passages first.
- Grounded generation
- Grounded generation instructs the model to answer only from retrieved passages, reducing invented facts.
- Citation handling
- Citations link each claim to its source passage so answers can be checked and trusted.
- RAG evaluation
- RAG evaluation measures retrieval hit rate and answer faithfulness on a labeled question set.
- Index operations
- Index operations cover updating, versioning, and scaling the vector store as documents change.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Answers cite wrong or irrelevant passages
Inspect top retrieved chunks for the failing query; shrink chunk size with overlap and retune retrieval filters.
Correct document exists but is never retrieved
Check embedding coverage and metadata filters, then re-chunk the missing document and verify it ranks in top results.
Model invents facts despite retrieved context
Tighten the prompt to answer only from provided passages and require citations per claim.
Large documents slow ingestion and search
Batch embedding calls, pre-filter by metadata, and scale the index shards before re-ingesting.
Quality drops as the corpus grows
Add a regression question set over old and new documents and rerank or prune stale passages.
Before you move on, you should be able to
- Design RAG pipelines balancing accuracy, latency, and operating cost
- Build document ingestion with chunking and embedding stages
- Deploy vector search with reranking and citation-aware answers
- Evaluate retrieval accuracy and groundedness on test sets
- Operate index updates and monitoring at scale
- Diagnose grounding failures from retrieval traces
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
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