About the Course
Organizations invest in RAG because they need AI results they can verify, not just outputs that sound plausible. To do that, you need capabilities in retrieval design, chunking strategy, source ranking, embedding selection, grounding checks, and evaluation using metrics such as Recall@K and MRR. This course anchors those capabilities in a practical understanding of retrieval and generation workflows, drawing on the structure of real-world RAG systems that combine a knowledge base, retriever, re-ranker, and large language model.
This context engineering and retrieval-augmented generation (RAG) course turns scattered concepts into a working system for document-grounded AI. You will learn how to map user prompts to source selection, configure lexical and semantic retrieval, decide when hybrid search adds value, shape context windows with chunking and metadata, and judge answer quality with retrieval and response metrics. In practice, you will build a RAG architecture diagram, a chunking policy, a retrieval evaluation sheet, and a prompt-and-context template. You will also be introduced to production concerns such as observability, logging, and monitoring, while practicing the parts most professionals must actually execute hands-on. This course teaches how to design RAG pipelines, evaluate retrieval quality, and structure context so you can improve accuracy and reduce hallucinations with evidence-based methods.
The pace fits professionals who work under real constraints such as limited clean data, mixed document formats, budget pressure, and fast-moving AI adoption. You will not be expected to engineer a production system from scratch in five days; instead, you will practice the operational decisions that make a prototype useful and the governance choices that make it defensible. That makes the course suitable for teams that need immediate value from existing content repositories, search stacks, and LLM workflows.
Target Audience
This course is designed for professionals who need to turn company knowledge into dependable AI answers using context engineering and retrieval-augmented generation (RAG).
- AI Product Manager responsible for RAG feature scoping and context quality requirements
- Machine Learning Engineer building retrievers, rerankers, and prompt pipelines
- Data Scientist evaluating Recall@K, MRR, and answer grounding performance
- Knowledge Engineer structuring document sources and metadata for retrieval
- Solution Architect defining RAG reference architecture and integration patterns
- Information Architect organizing source hierarchies, chunking rules, and metadata
- LLM Application Developer implementing prompt templates and retrieval workflows
- Search Engineer tuning lexical search, semantic search, and hybrid retrieval
- Business Analyst documenting knowledge source requirements and use-case scope
- AI Governance Lead reviewing retrieval transparency, source traceability, and evaluation evidence
Course Objectives
This course equips you to plan, execute, and measure context engineering and retrieval-augmented generation (RAG) initiatives that improve answer groundedness, support governance requirements, and strengthen AI delivery decisions.
- Assess RAG readiness using a knowledge-source inventory, retrieval baseline, and context-window constraints.
- Apply BM25, semantic search, and metadata filtering to a document-grounded retrieval challenge.
- Design a chunking strategy and metadata schema for a practical context engineering workflow.
- Build a prompt-and-context template that controls source selection and answer grounding.
- Calculate Recall@K, Precision@K, and Mean Reciprocal Rank for retrieval evaluation.
- Classify retrieval errors as chunking, ranking, query parsing, or generation issues.
- Evaluate a RAG pipeline against grounding, latency, and source-traceability requirements.
- Synthesize findings into a RAG improvement roadmap and stakeholder reporting brief.
Requirements & Prerequisites
Prerequisites required: working knowledge of generative AI concepts, basic familiarity with Python or no-code AI tooling, and practical exposure to documents, search, or knowledge repositories. You should be comfortable interpreting system outputs, reviewing evaluation metrics, and discussing data sources and access constraints. Coding is not mandatory for conceptual application, but it is helpful if you want to extend exercises using Python, embeddings, or vector database workflows. Advanced implementation topics are taught at an operational application level, with selective hands-on work rather than production engineering.
Professional and Organizational Impact
When you lead context engineering and retrieval-augmented generation (RAG) with credible data and practical strategies, you become a trusted driver of answer quality and AI system reliability.
- Build stronger confidence in retrieval design choices.
- Gain practical skill with chunking, embeddings, and hybrid search.
- Strengthen your ability to diagnose hallucination sources.
- Enhance prompt design for grounded, source-aware answers.
- Develop evaluation discipline using Recall@K and MRR.
- Position yourself as a credible RAG implementation contributor.
- Expand into AI product, ML engineering, or knowledge architecture roles.
Organizations that embed context engineering and retrieval-augmented generation (RAG) into AI delivery reduce unreliable answers, improve knowledge reuse, and build lasting competitive advantage.
- Reduce hallucination risk in document-grounded AI assistants.
- Lower rework caused by poor retrieval and weak context selection.
- Improve knowledge-base utilization across internal and customer-facing workflows.
- Strengthen auditability through traceable source selection and evidence logs.
- Increase search relevance using hybrid retrieval and metadata design.
- Support faster AI rollout with reusable context templates.
- Improve leadership confidence in measured retrieval performance.
Training Methodology
This is a practical, outcome-driven course designed to turn context engineering and retrieval-augmented generation (RAG) aspiration into measurable action and credible reporting.
Methodology includes:
- Calculate Recall@K and MRR from a retrieval evaluation dataset.
- Simulate a knowledge-assistant launch with latency, cost, and grounding constraints.
- Assess a sample RAG flow using a chunking checklist and retrieval rubric.
- Map source ownership, approval, and escalation across the AI reporting chain.
- Analyze case patterns from healthcare, e-commerce, finance, and legal search use cases.
- Build a context template and evaluation scorecard under time-boxed workshop conditions.
- Reflect on current retrieval practices using benchmark results and grounding evidence.
Upcoming Sessions
Next available dates worldwide
No international sessions scheduled
Certification
Recognized credentials that advance your career
Participants who complete the Context Engineering and Retrieval-Augmented Generation (RAG) Basics Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Effective Learning & Skill Development
- Build expertise with structured, outcome-driven learning.
- Equip individuals and teams with skills that grow with industry needs.
- Reinforce learning through real-world scenarios, case studies and practical exercises.
Career Growth & Professional Advancement
- Apply what you learn with a proven methodology that ensures lasting impact.
- Develop immediately usable skills that translate directly into workplace success.
- Gain the expertise needed for career advancement and leadership roles.
Training Optimization & Learning Excellence
- Tailor training to industry-specific challenges and organizational goals.
- Use data-driven insights and automation to enhance training effectiveness.
- Evaluate progress and ensure long-term learning success.
