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 with 3+ years of experience building production-grade retrieval augmented generation systems plus technical leads evaluating enterprise RAG bootcamp options for their organisations. 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 RAG bootcamp use cases: knowledge currency, citation discipline, hallucination control, and data sovereignty
- RAG architecture deconstructed end-to-end: ingest, chunk, embed, index, retrieve, augment, and generate every stage explained
- Where RAG fails: tabular reasoning, multi-hop questions, abstract synthesis, and complex aggregation the honest failure-mode map
- When NOT to build RAG: long-context model alternative, fine-tune alternative, and classical search alternative the decision criteria this production RAG training establishes
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Enterprise learning solutions built for corporate teams.
Go beyond standard classroom delivery with enterprise-ready learning infrastructure, managed execution, capability insights, and production-like practice environments designed for corporate scale.
Enterprise Command Center (LMS+)
Managed Batches (End-to-End Execution)
Capability Audits (Pre-Training Intel)
Custom Chaos Sandboxes
Demonstrable skills your team will apply on live projects.
Design and ship production RAG pipelines end-to-end
From corpus ingestion through citation-grounded generation with the appropriate vector store, embedding model, and hybrid retrieval strategy selected on technical merit. The core competency this retrieval augmented generation course delivers.
Apply hybrid retrieval training patterns that beat pure-vector baselines
BM25 plus dense vector plus cross-encoder re-ranker combinations with measured nDCG lift documented in the hybrid retrieval training lab the pattern most production RAG training systems require.
Evaluate RAG quality with RAGAS evaluation course discipline
Faithfulness scoring, context precision, context recall, and answer relevance instrumented as CI gates and applied systematically across every RAG change. The RAGAS evaluation course skill that defines production-grade quality.
Pass the RAG Certification exam
Two attempts included; cohort first-attempt pass rate 87%. A recognised RAG certification that supports individual career portfolios and positions graduates for senior RAG engineer salary India market roles.
Reduce RAG system latency and resource consumption by 40–60%
Re-ranker placement, embedding model selection, caching strategy, and chunk-size optimisation applied to the production RAG training capstone system with documented before-and-after measurements.
Ship a production-grade enterprise RAG bootcamp capstone
End-to-end production RAG application with RAGAS evaluation harness, observability pipeline, multi-tenant security, and red-team report the career-grade artefact this enterprise RAG bootcamp produces.
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, RAGAS evaluation, and observability the gap this production RAG training closes
- ·Limited fluency with embedding model selection, chunking strategies, and the measurable trade-offs between them the vector database training skills this programme builds from scratch
- ·No working knowledge of hybrid retrieval training cannot combine BM25, dense retrieval, and cross-encoder re-ranking into a production pipeline
- ·Cannot evaluate RAG system quality with discipline relies on subjective spot-checks rather than RAGAS evaluation course metrics: faithfulness, context precision, and answer relevance
- ·Unfamiliar with vector database training options at enterprise scale cannot choose between pgvector, Weaviate, Pinecone, Azure AI Search, and Milvus on technical merit
- ·No discipline for index lifecycle management, multi-tenant security, or observability in production RAG training environments the operational gaps this enterprise RAG bootcamp addresses
Where they'll arrive
- ✓End-to-end RAG architect designs ingestion, chunking, embedding, indexing, hybrid retrieval, re-ranking, and generation pipelines to the standard this retrieval augmented generation course demands
- ✓Hybrid retrieval training mastery combines BM25, dense vectors, and cross-encoder re-ranking for documented quality lifts over pure-vector baselines
- ✓RAGAS evaluation course graduate applies faithfulness scoring, context precision, context recall, and answer relevance as CI-gated production quality gates
- ✓Production RAG training practitioner implements multi-tenant security, index rotation, embedding caching, and end-to-end observability across the full pipeline
- ✓Vector database training fluency selects between pgvector, Weaviate, Pinecone, Azure AI Search, and Milvus on technical merit for each enterprise deployment context
- ✓RAG certification holder carries a credentialled qualification and a production capstone portfolio that supports RAG engineer salary India career progression
Built for L&D outcomes, not seat counts.
Retrieval augmented generation course depth, not demos
Every module is anchored by a production lab chunking A/B tests, hybrid retrieval training pipelines, RAGAS evaluation harnesses, and vector database training comparisons on real enterprise corpora.
Hybrid retrieval training the pattern most programmes skip
The dedicated hybrid retrieval training module covers BM25 plus dense retrieval plus cross-encoder re-ranking with measured nDCG lift the combination that consistently outperforms pure-vector RAG in production.
RAGAS evaluation course built into every lab
Unlike courses that treat evaluation as an afterthought, this enterprise RAG bootcamp CI-gates every pipeline change against a RAGAS evaluation harness the production RAG training standard that enterprise teams require.
Documented production outcomes not certificate completion
The capstone produces a publicly reviewable production RAG system with observability, multi-tenant security, and a red-team report the evidence that supports RAG engineer salary India career progression.
Vector database training vendor-neutral decision framework
The vector database training module benchmarks pgvector, Weaviate, Pinecone, Azure AI Search, and Milvus on recall, latency, and throughput giving graduates a defensible selection framework for any enterprise deployment.
LangChain RAG course patterns across all agentic modules
LangChain RAG course patterns document loaders, retrieval chains, agentic planner patterns, and conversational memory are woven through the labs so graduates can apply them immediately in their team's production stack.
A four-milestone path from skill gap to client-ready.
RAG architecture foundations and vector database training stack
Establish a working mental model of the full retrieval augmented generation course architecture ingestion, chunking, embedding, and indexing and complete the vector database training module comparing pgvector, Weaviate, Pinecone, and Milvus on a real enterprise workload.
Hybrid retrieval training, query understanding, and grounding labs
Learners complete the hybrid retrieval training module with BM25 and re-ranker labs, the query rewriting and HyDE module, and the citation-grounded generation lab each producing a measurable quality lift over the pure-vector baseline.
RAGAS evaluation course, advanced patterns, and production RAG training
Each learner builds a complete RAGAS evaluation harness with CI integration, completes the agentic and multi-hop RAG pattern modules, and implements multi-tenant security and observability the production RAG training standard the enterprise RAG bootcamp requires.
Production capstone and RAG certification sprint
Learners ship a production-grade RAG application with hybrid retrieval, RAGAS evaluation, observability, and a red-team report then complete the RAG certification preparation sprint with two exam attempts included.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
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Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·AI and ML engineers with 3+ years of production experience who want a structured retrieval augmented generation course that goes beyond tutorials into enterprise-grade pipeline design
- ·Senior software engineers and data engineers in India and globally who are building or evaluating production RAG training systems and need vector database training and hybrid retrieval training depth
- ·Solution architects who need to evaluate enterprise RAG bootcamp options and require a defensible technical framework for vector store selection, chunking strategy, and evaluation discipline
- ·Technical leads who have delivered a notebook RAG demo and now need to ship it to production with RAGAS evaluation, multi-tenant security, and observability the gaps this production RAG training closes
- ·Engineers targeting senior RAG engineer salary India roles who need a RAG certification and a production capstone portfolio as credible career evidence
Pre-requisites
- ·Python proficiency at an intermediate level is required learners should be comfortable with async patterns, API clients, and data structures before the LangChain RAG course labs begin
- ·Familiarity with at least one LLM API OpenAI, Anthropic, or Google is expected; the retrieval augmented generation course builds on this foundation from module one
- ·Basic SQL and database literacy is helpful for the vector database training modules covering pgvector and multi-tenant schema design
- ·Enterprise cohorts should bring a real production use case or corpus to the capstone the enterprise RAG bootcamp project delivers maximum value when grounded in the learner's actual domain
Trusted by L&D leaders across the world.
"We had been running a pure-vector RAG system in production for six months and hitting accuracy walls we could not explain. The hybrid retrieval training module specifically the BM25 plus re-ranker lab identified exactly where we were losing rare-term queries. We shipped the fix two weeks after the programme and our faithfulness score went from 0.71 to 0.89 on our RAGAS evaluation harness."
"The RAGAS evaluation course module changed how our team ships RAG changes. We had been doing manual spot-checks now every pipeline change is CI-gated against context precision and faithfulness thresholds. We caught a chunking regression in our first sprint after the programme that would have degraded production quality for weeks."
"I was sceptical that a retrieval augmented generation course could cover vector database training across five stores without being superficial. The vendor-neutral benchmarking lab same workload, same queries, five backends gave me exactly the selection framework I needed to justify pgvector to our platform team. The RAG certification gave me the credential to present the recommendation credibly."
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.