TVH produces professionals who build, defend, and operate production AI systems — with evidence to prove it.

Set up your vector database, embed your first corpus, and run semantic search queries that return relevant results. The first production artifact: a working retrieval pipeline you understand end-to-end.

Build a multi-format ingestion pipeline handling PDFs, Word docs, and web content. Chunking strategies, metadata tagging, and freshness contracts — so your retrieval always knows how old its data is.

Wire retrieval to generation. Build the full query pipeline — context assembly, LLM call, response grounding. First portfolio piece: an end-to-end RAG system answering questions from your own document corpus.

Add semantic caching and prompt engineering to cut token spend without degrading answer quality. Build a cost dashboard. Know what every query costs before your first cloud bill arrives.

Instrument your RAG system with distributed tracing and quality metrics. Build the observability layer that catches silent failures before users do — latency, retrieval relevance, error rate, all in one dashboard.

Dockerise your RAG system and deploy it to cloud. Environment parity between dev and production. The moment it runs on a server you don't own — that's when production thinking starts.

Add authentication, rate limiting, and input validation to your RAG API. Build the boundary layer that separates legitimate users from everything else. Your first production security evidence artifact.

Run your system under simulated production load. Find the breaking point before your users do. Document your P99 latency, your failure mode under 3× peak load, and your capacity plan.

Add BM25 keyword search alongside your vector search. Build the hybrid retrieval layer with reranking. Test which combination produces the highest answer quality on your evaluation set.

Package everything you built across M01–M09 into a portfolio-grade evidence pack. Write the architecture decisions document. Prepare the live defence. This is the PractaThon™ submission.

Build incremental indexing pipelines that update your vector store automatically when source documents change. Schedule-based and event-driven triggers. Production data freshness without manual re-indexing.

Implement quality scoring, duplicate detection, and drift monitoring in your data pipeline. Build the layer that prevents garbage from entering your vector store in the first place.

Build backup/restore procedures and blue-green deployment for your vector index. The first time your index is corrupted in production is not the time to figure out recovery. Build that capability now.

Implement automated PII scanning at ingestion and credential vault integration. Build the data privacy layer that prevents personal data leakage through your retrieval pipeline.

Build role-based permissions enforced at the retrieval layer — not just the application layer. Document-level security that prevents User A from accessing User B's data through semantic similarity queries.

Build tamper-proof audit logs and automated compliance reporting. Every query logged with who, what, when, and what was retrieved. The audit trail that makes your system defensible under examination.

Instrument every layer of your RAG system — request-level traces, code-level profiling, and end-to-end span correlation. Find latency bottlenecks before your P99 becomes your SLA breach.

Build custom business metrics beyond infrastructure monitoring — answer quality scores, user satisfaction signals, retrieval relevance trends. Add anomaly detection so your system alerts before users complain.

Build a systematic RAG quality measurement pipeline using RAGAS. Faithfulness, answer relevance, context precision — scored automatically against golden datasets you build and own.

Add experimentation infrastructure — test retrieval changes against your evaluation set before deploying. Automated quality gates that block a deployment if answer quality drops below threshold.

Build human feedback collection into your RAG system — thumbs up/down, correction capture, confidence-gated escalation. Close the loop between what your system produces and what users actually needed.

Package your L2 evidence into a production-grade portfolio positioned for ₹25–40L roles. Architecture decisions documented. Evaluation results quantified. System demo ready. PractaThon™ submission prepared.

Build query decomposition that breaks complex questions into parallel sub-queries executed simultaneously. The difference between a RAG system that answers and one that reasons.

Implement multi-hop retrieval — following citations, cross-references, and entity links across your knowledge base. Build the knowledge graph layer that makes your system find what it was not directly asked for.

Implement HyDE (hypothetical document embedding) and advanced reranking to close the gap between how users phrase queries and how your documents describe answers. Measure the uplift in retrieval quality.

Implement the ReAct (Reason + Act) pattern — agents that think through their plan before executing it. Build autonomous agents with governed stop conditions. The architecture that separates agents from scripts.

Build a sandboxed tool-calling system — code execution, API calls, database queries — with safety controls that prevent tool misuse. Every tool call logged, bounded, and reversible.

Coordinate specialised agent teams — a planner, retriever, critic, and executor working together on a single task. Build the orchestration layer that routes work, handles failures, and resolves conflicting outputs.

Build complete tenant isolation — one system, multiple organisations, zero cross-contamination. Separate vector namespaces, per-tenant authentication, and adversarial isolation testing to prove the walls hold.

Build per-tenant configuration management — custom prompts, model selection, retrieval parameters, feature flags — without code deployments. Shared infrastructure, the illusion of a dedicated system per tenant.

Implement vector index sharding and auto-scaling policies. Run load tests at 500-tenant volume. Document the architecture decisions that allow horizontal growth without re-engineering the foundation.

Build per-tenant usage metering and automated billing. Track every query, token, and byte per tenant. Connect to Stripe for automated invoicing. Cost allocation accurate to within ±5% of actual bills.

Connect your RAG system to enterprise data sources — CRMs, ERPs, databases — through a governed integration layer. Build the API abstraction that allows new data sources without touching the retrieval architecture.

Build SOC 2 controls documentation, GDPR-compliant data subject rights procedures, and automated evidence collection. The compliance artefact that survives enterprise procurement audit.

Package your complete L3 evidence into a MasteryX portfolio. Architecture decisions across all 12 frameworks documented and defensible. Go-to-market positioning prepared. PractaThon™ submission with 1:1 Architecture Review.