HIGAET RAG Application Engineering
Engineer retrieval-augmented generation systems covering chunking, embeddings, hybrid search, reranking, citations, and grounded answer evaluation.
Duration
8 weeks · 6-8 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Retrieval-augmented generation (RAG) engineering builds assistants that answer from your own documents by retrieving the right passages before generating a grounded reply. It matters now because teams need AI answers they can trust, check, and trace back to sources.
Support teams, operations staff, and knowledge workers use RAG for document Q&A, policy lookup, and research over curated collections. It solves grounding answers in current sources with citations, but it does not fix missing, outdated, or messy source data — if the documents are absent or wrong, retrieval cannot invent the truth.
By the end you will be able to build a document ingestion pipeline with parsing, cleaning, and chunking, a hybrid search service with dense, lexical, and reranking stages, and a grounded Q&A API with citations and source links.
Why this course exists
The gap is between a demo chatbot that guesses fluently and a production RAG system that ingests, chunks, indexes, reranks, cites, and evaluates answers over real documents. This course teaches the arc from Model and Prompt through Context and Retrieval to Evaluation, Security, Infrastructure, and Production, so students can ship grounded Q&A that holds up.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST APIs
- Familiarity with embeddings and databases
- Basic text processing knowledge
Technologies & tools
Skills you'll gain
A 8 weeks arc, module by module.
- Module 01
Module 01 — Foundations: RAG architectures and grounding concepts
- Module 02
Module 02 — Core: Document parsing, cleaning, and chunking strategies
- Module 03
Module 03 — Core: Embeddings, vector stores, and metadata design
- Module 04
Module 04 — Engineering: Hybrid search, filters, and rerankers
- Module 05
Module 05 — Engineering: Grounded answering with citations
- Module 06
Module 06 — Advanced: RAG evaluation, failure analysis, and tuning
- Module 07
Module 07 — Production: Refresh pipelines, access control, and monitoring
- Module 08
Module 08 — Capstone: Ship a cited Q&A app over a document collection
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.
- Build document ingestion pipelines with parsing, cleaning, and chunking
- Design embedding workflows and vector index schemas
- Develop hybrid search with dense, lexical, and reranking stages
- Deploy grounded Q&A APIs with citations and source links
- Integrate access filters, metadata routing, and refresh jobs
- Evaluate faithfulness, recall, and answer relevance
- Secure indexes with tenant isolation and redaction rules
- Optimize chunk size, top-k selection, and query latency
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Document ingestion and chunking pipeline
- Project 02
Hybrid search with reranking service
- Project 03
Citation-grounded Q&A API
- Capstone
Grounded retrieval Q&A application with evaluation
Speak the language first.
- Document ingestion
- Parsing and cleaning source files so their text is ready for indexing and search.
- Chunking
- Splitting documents into overlapping passages sized so each one carries enough context for retrieval.
- Embeddings
- Numeric representations of text that let a system find passages by meaning rather than exact words.
- Vector index schemas
- The organized structure of a vector store, including fields and metadata used for filtering.
- Hybrid search
- Combining meaning-based dense search with keyword-based lexical search for better recall.
- Reranking
- Re-scoring top search hits with a stronger model so the most relevant passages answer first.
- Citations
- Source links attached to generated answers so readers can verify each claim.
- Grounded answers
- Responses built strictly from retrieved passages rather than the model guessing from memory.
- Grounded answer evaluation
- Checking answers for faithfulness to sources and flagging unsupported statements.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Answers cite irrelevant passages
Tighten chunk size and overlap, add metadata filters, and test whether a reranking stage restores relevance.
Key facts are missing from retrieval
Check parsing output for dropped tables or text, adjust chunk boundaries, and confirm the embedding model covers the domain.
Hybrid search underperforms dense-only search
Compare dense, lexical, and fused rankings on a test query set, then retune the fusion weights toward the stronger signal.
Generated answers lack citations
Require the prompt to quote source identifiers per claim and reject responses that omit them during testing.
Index is slow as documents grow
Review index settings and metadata filters, then partition the collection or pre-filter by source before search.
Before you move on, you should be able to
- Build document ingestion pipelines with parsing, cleaning, and chunking
- Design embedding workflows and vector index schemas
- Develop hybrid search with dense, lexical, and reranking stages
- Deploy grounded Q&A APIs with citations and source links
- Evaluate grounded answers for faithfulness to sources
- Explain how chunking and retrieval shape answer quality
- Build citation checks into answer generation
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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Ready to start HIGAET RAG Application Engineering?
A 8 weeks course — AI & Generative Intelligence.