Relevant context is inconsistent
Learn how indexing, chunking, embeddings, query strategy, and reranking affect the answer.
Course 02 · 5 days
RAG Engineer Sprint
Design, evaluate, and deploy retrieval-augmented generation systems that give GenAI applications better grounding and better answers.
1 · Learner problem
A RAG demo can answer questions; a RAG system must earn trust.
Retrieval quality, chunking, evaluation, multimodal content, and deployment choices determine whether a knowledge application works outside a notebook.
Good answers are hard to measure
Design a practical evaluation loop for retrieval and generation instead of relying on a few happy-path examples.
Knowledge is not only text
Explore multimodal, graph, and agentic retrieval patterns when a basic vector search is not enough.
A production RAG plan
Connect the pipeline, evaluation, deployment, and operating notes in a demonstrable project.
2 · Course outcome
Leave able to reason about retrieval quality and production trade-offs.
First RAG pipeline
Ingest, chunk, embed, retrieve, and generate from a defined knowledge source.
Advanced retrieval
Compare retrieval strategies and improve context relevance for real questions.
Measure system quality
Use evaluation examples to inspect retrieval and generation instead of guessing.
Plan deployment
Document production RAG architecture, monitoring, failure modes, and the next engineering step.
For engineers building knowledge-grounded applications.
- RAG and GenAI engineers
- Software engineers working with LLM applications
- ML engineers moving from prototype to production
- Technical builders with a knowledge application problem
Bring a question set and a source of truth.
Programming and basic LLM familiarity are useful. The sprint focuses on system design and evaluation, not a no-code introduction.
4 · Curriculum
Six modules for production RAG thinking.
- Build Your First RAG PipelineConnect ingestion, chunking, embeddings, retrieval, and generation.
- Advanced Retrieval EngineeringImprove query handling, ranking, context selection, and answer grounding.
- Production RAG + EvaluationDefine evaluation examples and operating checks for quality and reliability.
- Multimodal, Graph & Agentic RAGUnderstand alternatives when text-only retrieval is not enough.
- Production RAG System + DeploymentPlan deployment, observability, security, cost, and maintenance.
5 · Live project
Build a production RAG system and deployment plan.
Use a trusted document set, knowledge base, or product-support corpus to show retrieval quality and answer grounding.
Knowledge ingestion
Prepare sources, chunk content, and create a retrieval path that can be inspected.
Question and quality set
Test retrieval relevance, answer grounding, and failure cases against representative questions.
Production RAG plan
Explain architecture, monitoring, access, cost, and the next deployment milestone.
6 · Trainer credentials
View trainer profileRetrieval engineering taught with evaluation attached.
MHGrad’s practice includes RAG, enterprise knowledge applications, agents, MCP, software delivery, and responsible AI workflows. Verified trainer credentials and references are available during enrolment conversations.
Five-day live intensive
- Daily retrieval and evaluation instruction
- Guided system build and quality review
- Deployment planning and final demonstration
- Exact dates, timezone, and delivery format confirmed before enrolment
8–10 · Fees and enrolment
Current fee shared on consultation.
Ask about the next sprint, availability, payment steps, or a private team delivery. Refund terms are published before payment.
Enquire for current fee
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FAQ and refund terms
Before you enrol.
Do I need to know embeddings or vector databases?
Basic GenAI or software familiarity helps, but the sprint introduces the RAG system path before moving into advanced retrieval and evaluation.
Will the project use my company data?
Only use data you are authorised to share. A guided, non-confidential corpus can be used when a private data path is not approved.
Can a team take the sprint privately?
Yes. Ask about a delivery adapted to your knowledge source, security boundaries, and engineering stack.
What is the refund policy?
Eligibility, cut-off dates, transfers, and cancellations are stated in the refund policy and enrolment confirmation.
Make your RAG system measurable and ready for its next stage.
Ask for dates, fee, prerequisites, or a team-training conversation.