AI & Data · Advanced Specialist
Generative AI Engineering
Ground the answer. Measure the quality. Control the action.
Engineer reliable AI applications—not just prompts.
Advanced layer includedIndustry Edge tools and scenario-based labs beyond the recognized core. Curriculum v1.1 • Updated Jul 2026
Recognized curriculum, prerequisites, assessments, and public fees disclosed before counselling.
Industry Edge Lab includedAdvanced tools, emerging patterns, and scenario-based labs beyond the recognized core.Registration and admissions follow-up continue securely in BharatCampus ONE.
Included beyond the published curriculum
The recognized core is your foundation—not the finish line.
Every Generative AI Engineering cohort also enters the CohortAI Industry Edge Lab: an evolving layer of advanced tool categories, emerging architecture and production patterns, and scenario-based labs. A guided counselling preview connects the most relevant capabilities to your target role, followed by a release-governed lab sequence after enrollment.
Release-governed advanced tools Emerging architecture patterns Scenario-based labs
Fit before enrollment
Who should join
Python developers, data professionals, full-stack learners and graduates who want to build dependable generative-AI products.
Role pathways
Your starting point
Python fundamentals, APIs and Git. A short bridge is provided for learners who need revision.
This is not an exclusion filter. It helps us confirm whether you can start directly or would benefit from a short bridge before the cohort.Recognized core
Skills and technology stack
These employability foundations are part of the public learning promise and remain visible before registration.
Python LLM APIs Prompt and context engineering Structured outputs Embeddings Vector-search concepts RAG Evaluation Guardrails Tool-connected workflows Docker/cloud deployment
Build-first curriculum journey
9 missions. One defensible portfolio.
Mentor-led hybrid learning with 1.5-hour live instruction, all-day supervised practice access, weekly build evidence and a version-locked capstone.
M011 week · 7.5 live hours
LLM foundations and responsible AI
Reality Check Lab: identify when fluent output is still unsafe or wrong.
- Tokens, embeddings and transformer intuition
- Pre-training, instruction tuning and inference
- Capabilities, limitations and hallucination
- Privacy, copyright and sensitive information
Guided labEvaluate several model responses for accuracy, risk, uncertainty and suitable human control.
Portfolio evidenceAI-use case canvas, risk register and model-behaviour report.
AssessmentResponsible-AI review and concept assessment.
M021 week · 7.5 live hours
Prompt and context engineering
Prompt Arena: improve reliability without hiding errors behind longer instructions.
- Instruction design and role/context separation
- Few-shot examples and response constraints
- Decomposition and reasoning scaffolds
- Context selection and compression
Guided labCreate and benchmark a versioned prompt suite for extraction, classification and assisted writing.
Portfolio evidencePrompt catalogue, test cases and evaluation scorecard.
AssessmentBlind prompt comparison and injection-resilience check.
M032 weeks · 15 live hours
Python APIs, structured outputs and orchestration
Zero-Parse-Failure Challenge: turn probabilistic text into dependable application data.
- LLM API calls, authentication and retries
- Streaming and conversational state
- JSON/schema-constrained output
- Validation and error recovery
Guided labBuild a multi-step document-processing service with validated structured output and retry logic.
Portfolio evidenceAPI service, schemas, automated tests and run-cost report.
AssessmentStructured-output reliability practical.
M042 weeks · 15 live hours
Embeddings, vector search and RAG
Find–Ground–Prove: every important claim must be traceable to evidence.
- Embedding intuition and similarity
- Chunking and document preparation
- Vector indexes and metadata filters
- Retrieval, reranking and grounded generation
Guided labBuild a cited knowledge assistant over a controlled document collection and diagnose retrieval failures.
Portfolio evidenceRAG service, ingestion pipeline, citation UI and retrieval evaluation report.
AssessmentGrounded-answer test with hidden questions.
M051 week · 7.5 live hours
Evaluation, guardrails and observability
AI Quality Gate: block a release when helpfulness improves but safety or grounding falls.
- Quality dimensions and acceptance criteria
- Golden datasets and regression tests
- Human, heuristic and model-based evaluation
- Safety filters and policy checks
Guided labCreate an automated evaluation harness and red-team a GenAI workflow before release.
Portfolio evidenceEvaluation dataset, quality dashboard, safety test pack and release gate.
AssessmentRed-team/blue-team evaluation exercise.
M062 weeks · 15 live hours
Tool-connected AI and agent workflows
Human-in-the-Loop Mission: automate useful work without giving the model unlimited agency.
- Tools, actions and function calling
- Planning versus deterministic workflow design
- State, memory and task boundaries
- Tool permissions and human approval
Guided labBuild a tool-connected assistant that performs a bounded business workflow with approval gates.
Portfolio evidenceAgent workflow, tool contracts, permission matrix and execution trace.
AssessmentFailure-mode demonstration and control review.
M071 week · 7.5 live hours
Fine-tuning and adaptation concepts
Adapt or Retrieve? defend the least complex method that meets the requirement.
- When prompting or RAG is enough
- Dataset preparation and quality
- Supervised fine-tuning concepts
- Parameter-efficient adaptation
Guided labPrepare and evaluate a small adaptation dataset and choose between prompting, RAG and fine-tuning.
Portfolio evidenceAdaptation decision memo, dataset card and comparative evaluation.
AssessmentArchitecture trade-off viva.
M081 week · 7.5 live hours
Deployment, security and cost control
Token Economics Lab: improve user value per rupee without quietly degrading quality.
- API/service architecture and queues
- Caching, batching and streaming
- Authentication, tenant isolation and secrets
- Rate limits and abuse prevention
Guided labDeploy a secure multi-user AI service with budgets, telemetry, fallbacks and operational runbook.
Portfolio evidenceContainerised service, threat model, cost model and operations dashboard.
AssessmentProduction-readiness and abuse-case review.
M091 week · 7.5 live hours
GenAI application capstone
Trustworthy AI Demo Day: show value, evidence, risk controls and unit economics.
- Product problem and success metrics
- Data and retrieval architecture
- Workflow and user experience
- Evaluation, safety and human controls
Guided labBuild and deploy a complete generative-AI product for a real workflow, with evaluation evidence and controls.
Portfolio evidenceProduct repository, architecture, eval report, threat model, cost model and live demo.
AssessmentAI Product Review Board and GenAI interview simulation.
The week feels different here
A repeatable rhythm from concept to evidence
Weekly rhythm
- 1Concept sprint and visual roadmap
- 2Guided implementation lab
- 3Independent build challenge
- 4Debug, review and production-pattern clinic
- 5Skill check, demo and learning reflection
Learner experience
CohortAI Skill Passport with milestone badges
Build-first weekly checkpoints rather than lecture-only completion
Peer demo and code/design review rituals
Mentor office hours and all-day practice-lab access
Capstone Demo Day with a business narrative, technical walkthrough and interview-style defence
Responsible AI-assisted learning policy: explain, verify and own every submitted artifact
Interview-ready proof
What you will be able to show—not merely claim
These are tangible outputs you can demonstrate, explain, and defend through a technical walkthrough, business narrative, or interview conversation.
Structured-output AI assistant
Grounded knowledge application with citations
Evaluation and safety harness
Tool-connected workflow prototype
Deployed GenAI product capstone
Transparent evaluation
How your progress is assessed
Completion standard
Minimum 70% overall, mandatory capstone pass, at least 80% lab submissions and versioned portfolio evidence.
CohortAI Industry Edge Lab
Your published curriculum is the foundation. The Industry Edge is what keeps it moving forward.
The AI Frontier Lab extends the program with interoperable agent patterns, advanced retrieval, multimodal workflows, model routing, observability and adversarial evaluation.
Your counselling preview connects relevant capability categories to your target role. The complete release-governed lab sequence becomes part of the enrolled learning plan.Version-controlled learning promise
GENAI@1.1.0
Released 24 July 2026 under catalog CAT-2026.09.1. This page presents the approved curriculum, learner outcomes, duration, and public fee for informed comparison.
Straight answers
Frequently asked questions
Who is the Generative AI Engineering program designed for?
Python developers, data professionals, full-stack learners and graduates who want to build dependable generative-AI products.
What prerequisites do I need?
Python fundamentals, APIs and Git. A short bridge is provided for learners who need revision.
What is included in the public curriculum?
The public curriculum includes the recognized stack, all 9 module themes, guided labs, portfolio artifacts, assessment model, duration, and public fees. The Industry Edge Preview explains how advanced capability categories extend this foundation for your target role.
Where do counselling and enrollment happen?
CohortAI supports public discovery and program comparison. Registration, consent, counselling follow-up, application updates, and enrollment are managed securely in BharatCampus ONE.