Retrieval-Augmented Generation (RAG) Engineering
capability building,
designed for your organisation.
A custom-built corporate programme for AI engineers, ML engineers, senior software engineers, data engineers, and solution architects (3+ years) building production-grade retrieval-augmented generation systems for enterprise use cases. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Why RAG beats fine-tuning for most enterprise use cases — knowledge currency, citation, hallucination control, data sovereignty
- RAG architecture deconstructed: ingest → chunk → embed → index → retrieve → augment → generate
- Where RAG fails: tabular reasoning, multi-hop questions, abstract synthesis, complex aggregation
- When NOT to build RAG: long-context alternative, fine-tune alternative, classical search alternative
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Demonstrable skills your team will apply on live projects.
Design and ship production RAG pipelines
From corpus ingestion through citation-grounded generation, with appropriate vector store, embedding model, and retrieval strategy chosen on merit.
Apply hybrid retrieval and re-ranking
BM25 + dense + cross-encoder re-ranker combinations that consistently outperform vector-only baselines.
Evaluate RAG quality with discipline
RAGAS, faithfulness scoring, context precision/recall — instrumented as CI gates, not subjective spot-checks.
Pass GSDC RAG Engineer certification
Two attempts; cohort first-attempt pass rate 87%.
Reduce RAG costs by 40-60%
Re-ranker placement, embedding model selection, caching, and chunk-size optimisation applied to capstone systems.
Ship production capstone
End-to-end production-grade RAG application with evaluation harness, observability, and red-team report.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Has built a notebook-grade RAG demo but never shipped one to production with citations, evaluation, and observability
- ·Limited fluency with embedding models, chunking strategies, and the trade-offs between them
- ·No working knowledge of hybrid retrieval, re-ranking, or query rewriting patterns
- ·Cannot evaluate RAG system quality with discipline — relies on subjective spot-checks rather than RAGAS, faithfulness, context precision
- ·Unfamiliar with vector store options at scale — pgvector, Weaviate, Pinecone, Azure AI Search, Milvus
- ·No discipline for index lifecycle, multi-tenant security, or cost optimisation in production RAG
Where they'll arrive
- ✓End-to-end RAG architect — designs ingestion, chunking, embedding, indexing, retrieval, re-ranking, and generation pipelines
- ✓Hybrid retrieval mastery — combines BM25, dense vectors, and re-ranking for measurable quality lifts over pure-vector baselines
- ✓Evaluation discipline — RAGAS, faithfulness, context precision/recall, answer relevance — applied with CI gates
- ✓Production patterns — multi-tenant security, index rotation, cost optimisation, observability across the full pipeline
- ✓Vector store fluency — chooses between pgvector, Weaviate, Pinecone, Azure AI Search, and Milvus on technical merit
- ✓Citation & grounding — every generated answer grounded in retrieved sources with verifiable citations
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
RAG, or Retrieval-Augmented Generation, connects an LLM to external knowledge sources and retrieves relevant information at query time before generating an answer. Fine-tuning retrains or adapts a model on a specific dataset so it performs better for a particular task, tone, format, or domain. In simple terms, use RAG when the model needs updated or private knowledge, and use fine-tuning when the model needs to behave differently or perform a specialised task more consistently. Microsoft describes RAG as retrieval plus contextual priming, while fine-tuning retrains the model on a smaller, specific dataset.