Project 01
Intelligent Document Processor API
Async FastAPI service that turns messy PDFs into validated structured data — built like Klarity, Hyperscience, and Eigen.
FastAPIPydanticOpenAIClaudePostgresDocker
Vector 1.0 · Starts 18 Jun 2026
Crack senior Gen-AI Developer interviews
Even with Zero Gen-AI Experience.
Sixty days. Five production projects. Five mock interviews. Built and taught by a Sr. Gen-AI Developer.
- Starts 18 Jun 2026
- ·
- Vector 1.0 · capped at 150 engineers
- ·
- Registration closes on 13 Jun 2026
What you walk away with
Sixty days, four hard outcomes.
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Why this is different
What Sets Vector 1.0 Apart From Other Gen-AI Courses
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How it works
Three things. That change everything.
A practitioner-led path with real builds and real interviews — designed so by day 60 your GitHub, resume and confidence are all interview-ready.
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Build it. Defend it. Get hired. 60 days.
The stack
Tools that show up in real 2026 Gen-AI job descriptions.
All taught hands-on with production patterns — never as toy notebooks.
15 tools · 4 layers
LangChain
Agent framework
LangGraph
Stateful agents
Pydantic
Data validation
OpenAI
GPT models
Anthropic
Claude models
MCP
Tool protocol
HuggingFace
Open models
Pinecone
Vector DB
Qdrant
Vector DB
Weaviate
Vector DB
RAGAS
RAG evals
AWS Bedrock
Inference cloud
FastAPI
Python API
Docker
Containers
Postgres
+ pgvector
+ production extras
LangSmith
GitHub Actions
CloudWatch
Step Functions
OpenTelemetry
AWS S3
AWS Bedrock
AWS Lambda
AWS API Gateway
Capabilities
Six capabilities that get you hired.
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Curriculum
Six sprints. Five projects. One offer letter.
You'll learn
- — How LLMs actually work — attention, tokenization, context windows, KV cache
- — Prompt engineering patterns that survive in production
- — Function calling, tool use, and structured outputs with Pydantic + Instructor
- — Production Python — async/await, FastAPI, error handling, observability
- — OpenAI, Anthropic Claude, and open-source model APIs side-by-side
- — Cost, latency, and reliability — the three things every Gen-AI engineer optimizes
Project shipped
Intelligent Document Processor API
An async FastAPI service that ingests messy PDFs, extracts schema-validated structured data with confidence scoring, and routes low-confidence extractions to a review queue.
You'll learn
- — LangChain core — runnables, LCEL, and the | operator composition pattern
- — Document loaders, text splitters, and the retriever interface
- — RAG primer — embeddings, vector stores, and naive retrieval (deep dive in Sprint 2)
- — LangGraph state design — typed dicts, reducers, and channel patterns
- — Persistence with SQLite and Postgres checkpointers for long-running agents
- — Streaming modes — values, updates, messages — and when to use which
- — Conditional edges, parallel branches, and cycle control
- — Human-in-the-loop with interrupts and clean resume semantics
- — LangSmith tracing for debugging stateful workflows in production
Project shipped
Stateful Multi-Step Research Agent API
A LangGraph-powered research API that runs multi-step investigations, recovers from tool failures, persists state across long-running runs, and pauses for human approval at critical checkpoints — exposed via FastAPI with SSE streaming.
You'll learn
- — Vector databases compared — Qdrant, Chroma, Weaviate, pgvector
- — Chunking strategies that respect semantic boundaries
- — Embedding model selection — OpenAI, Cohere, BGE, Voyage AI
- — Hybrid search — BM25 + dense retrieval with reciprocal rank fusion
- — Cross-encoder reranking and the latency-vs-accuracy tradeoff
- — Query understanding — query expansion, HyDE, multi-query patterns
- — RAGAS evaluation — faithfulness, answer relevancy, context precision and recall
- — Building golden datasets and integrating evals into CI/CD
- — Cost engineering — caching strategies, batch embedding, model routing
Project shipped
Enterprise RAG Service with CI Evaluation
A production-grade RAG API over a real document corpus with hybrid search, cross-encoder reranking, RAGAS evaluation in CI that blocks regressions, structured logging, and a written architecture decision record.
You'll learn
- — Agent design patterns — ReAct, Plan-and-Execute, Reflexion, Tree-of-Thoughts
- — Tool design that supports agent reasoning instead of breaking it
- — MCP protocol deep-dive — resources, tools, prompts, and transports
- — Building MCP servers in Python with the official SDK
- — MCP Inspector for testing and debugging servers
- — Multi-agent patterns — supervisor, hierarchical, and peer-to-peer
- — Loop control, cost ceilings, and graceful failure modes
- — Guardrails for production agents — input validation, output filtering, and safety rails with Guardrails AI and NeMo Guardrails
- — Tool authentication, rate limiting, and structured error semantics
- — Observability across agent runs — what to log, what to trace
Project shipped
Production MCP Server + Multi-Agent Orchestrator
A spec-compliant MCP server with auth, rate limiting, and observability — plus a Python multi-agent orchestrator that consumes it with supervisor-pattern routing across specialist agents. Both deployed as FastAPI services.
You'll learn
- — LLM fine-tuning fundamentals — LoRA, QLoRA, and when to fine-tune vs RAG vs prompt engineering
- — Hands-on fine-tuning — fine-tune a small open-source model with HuggingFace + Unsloth for a real use case
- — Corrective RAG (CRAG) — self-grading retrievers and re-query loops
- — Self-RAG — reflection tokens and source-aware generation
- — Adaptive RAG — query routing across retrieval strategies
- — Graph RAG — knowledge graphs as a retrieval substrate
- — Multi-modal RAG — tables, images, and structured data alongside text
- — Long-context vs RAG — when to choose each, and hybrid approaches
- — Conversational RAG — multi-turn retrieval with conversation memory
- — Production failure modes — handling conflicting sources and stale data
- — Evaluation for agentic RAG — grading the loops, not just the answers
Project shipped
Agentic RAG Core (deployed to AWS in Sprint 5)
Build the agentic RAG core in this sprint with corrective retrieval and self-grading loops. Sprint 5 takes it to AWS Bedrock with full observability — one project, built across two sprints, exactly how real engineering teams ship.
You'll learn
- — AWS Bedrock — model access, IAM patterns, cross-region considerations
- — AWS Knowledge Bases vs custom RAG — when each wins
- — Lambda for serverless agents — cold start strategies and warm pool patterns
- — Step Functions for agent orchestration with retries, timeouts, error catches
- — OpenSearch Serverless for vector storage at production scale
- — API Gateway, IAM, and VPC patterns for enterprise deployment
- — CloudWatch dashboards for cost-per-request, p95 latency, and error rates
- — Cost engineering on AWS — prompt caching, model routing, batch APIs
Project shipped
Agentic RAG on AWS Bedrock — End-to-End
Deploy your Sprint 4 agentic RAG core on AWS with Bedrock, Step Functions orchestration, OpenSearch vector storage, and CloudWatch dashboards — with an architecture decision record built for senior interview rounds.
You'll learn
- — 5 mock interviews per student with detailed written feedback
- — 100 Gen-AI interview questions across LLMs, RAG, agents, MCP, AWS
- — Live system design rounds — customer support agent, document platform, multi-tenant RAG
- — Resume rewrite focused on Gen-AI projects with quantified outcomes
- — GitHub portfolio polish — READMEs, architecture docs, demo videos
- — LinkedIn optimization for Gen-AI recruiter visibility
- — Behavioral interview prep with the STAR method applied to Gen-AI work
- — Salary negotiation playbook for senior Gen-AI roles in India and remote markets
Project shipped
Capstone — Your Architecture, My Review
You pick the problem, architect the solution, and ship it. I review every architectural decision in a one-on-one session designed exactly like a senior interview round.
You'll learn
- — CrewAI — role-based multi-agent design, sequential and hierarchical processes, and how it compares to LangGraph
- — AutoGen — conversational multi-agent patterns, group chat orchestration, and when to pick it over LangGraph
- — Building the same multi-agent workflow in LangGraph vs CrewAI — a side-by-side comparison
- — LlamaIndex — document indexing, retrieval pipeline differences from LangChain, and when to choose which
- — Azure OpenAI Service — managed endpoints, Azure AI Search for RAG, and how it differs from direct OpenAI API access
What you ship
Five projects on your GitHub by day 60.
Project 02
Stateful Multi-Step Research Agent
Long-running LangGraph agent with persistence and human approval — patterns Perplexity and Claude Projects use under the hood.
LangGraphPostgresFastAPILangSmithClaude
Project 03
Enterprise RAG Service with CI Eval
Hybrid retrieval with RAGAS evaluation that blocks regressions in CI — the kind of system Glean and Notion AI run.
FastAPIQdrantRAGASCohere RerankGitHub Actions
Project 04
MCP Server + Multi-Agent Orchestrator
Spec-compliant MCP server with a multi-agent client — production patterns Anthropic, Cursor, and enterprise teams build internally.
MCPFastMCPFastAPIAnthropicRedisOpenTelemetry
Project 05
Agentic RAG on AWS Bedrock
Self-correcting RAG fully deployed on AWS BedRock — the system AWS itself uses to demo Bedrock to enterprise.
AWS BedrockLambdaStep FunctionsOpenSearchS3
All five projects: deployed, documented, and on your GitHub by day 60.
Your instructor
Taught by a Sr. Gen-AI Developer who actually ships.
Prateek Mishra
Sr. Gen-AI Developer Working at a global MNC
Founder & instructor · TechSimPlus
Verified instructor profile on LinkedIn
12000+
Engineers taught
8
Years Of Experience
20+
Production projects
I build production Gen-AI systems for a living and have shipped what most courses only describe in slides — agentic RAG pipelines on AWS Bedrock, MCP servers in production, multi-agent systems with stateful checkpointing.
I started TechSimPlus because the gap between tutorial-grade Gen-AI and what actually ships in production was getting wider, not smaller. Ten thousand engineers later, that gap is still there. Vector 1.0 closes it.
I'm not a full-time creator. I'm a practitioner who teaches. Every project in this sprint is something I have architected at work or for clients.
Stack I ship in production
- LangGraph
- MCP
- Agentic RAG
- AWS Bedrock
- Pydantic
- FastAPI
- Pinecone
- Qdrant
- Step Functions
- RAGAS
Mock Interviews
The five rounds that will save your senior interview.
Five 1-on-1 mock interviews scheduled with you across Vector 1.0. Each one ends with written feedback you can read on the train home.
Round metric
5
1:1 mock interviews
Round metric
Real
Production-grade questions
Round metric
24h
Written feedback turnaround
Round metric
Senior
Calibrated to senior rounds
Five rehearsed rounds.One real one. Walk in calibrated.
Format
Live program. Real deadlines. Lifetime access.
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✓Private Discord community
✓5 mock interviews
✓Lifetime access to recordings
Pricing
One price. Limited seats.
Limited seats · Vector 1.0
Vector 1.0
Vector 1.0 is capped at 150 engineers
₹21,000
₹34,999
Save ₹13,999Registration closes in
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Everything included
- 60 days of live program instruction
- 5 production-grade Gen-AI projects
- 5 mock interviews with feedback
- 100 interview questions drilled
- Resume rebuild + LinkedIn optimization
- Architecture decision templates
- Private Discord community
- Lifetime access to all recordings
- Certificate of completion
- Alumni network
3-day no-questions-asked refund · Secure checkout