Modern Route-Full Stack GenerativeAI And Agentic AI Bootcamp

Understand what powers modern LLMs from the ground up. Learn transformer architecture, how attention works, and why LLMs generalize so well. Build strong fundamentals in tokenization, text encoding, embeddings, vector similarity, and the evolution from classical NLP to modern contextual representations.

Navigate the complete landscape of modern models — LLMs vs SLMs vs Multimodal models. Compare and understand major model families like GPT, Gemini, Claude, LLaMA, Mistral, Qwen, plus efficient SLMs like Phi & Gemma. Learn a practical model selection framework based on task type, latency, cost, deployment feasibility, and modality (text / code / vision).

Work hands-on with real commercial API ecosystems like OpenAI, Anthropic, Gemini, Groq, and Open Router. Learn how to structure production calls (system/user prompts, parameters like temperature/max tokens), implement streaming responses, handle retries, and track token usage. Build abstraction layers to switch providers seamlessly without rewriting your application logic.

Go beyond prompt engineering and learn how to adapt models for your own domain. Master full fine-tuning vs parameter-efficient fine-tuning (PEFT), including LoRA and QLoRA. Learn dataset preparation (instruction formatting, splits, cleaning), modern tooling like Hugging Face Transformers + PEFT, Unsloth, Axolotl, and advanced concepts like RLHF, DPO/ORPO/GRPO (concept positioning + application).

Build complete Retrieval-Augmented Generation pipelines that reduce hallucinations and deliver grounded answers. Learn ingestion and parsing (PDFs, docs, web), chunking strategies, embedding selection, metadata filtering, and vector DB integrations with Pinecone/Qdrant/Chroma. Implement robust retrieval workflows including similarity search, MMR, and cross-encoder re-ranking, plus citation-aware prompting to ensure trustable outputs.

Take RAG from demo → production. Learn context engineering, context window optimization, caching strategies (response cache, embedding cache, CAG), and reliability evaluation methods (faithfulness, relevance, retrieval quality). Extend systems to multimodal RAG (text + image grounding with vision-language workflows), and learn how to debug common RAG failure modes like bad chunking, noisy retrieval, missing context, and overlong prompts.

Design autonomous AI systems that do more than chat. Learn how to build single-agent and multi-agent architectures, including supervisor, hierarchical, and network-based systems. Implement tool use (APIs, functions, search, RAG), memory + state management, role prompts (planner/executor), human-in-the-loop approvals, loop prevention mechanisms, and cost-aware execution budgeting. Learn orchestration layers and state-graphs through frameworks like LangGraph (and CrewAI/AutoGen conceptually).

Ship production-safe GenAI applications with quality control. Learn observability (logging/tracing prompts, context, tools) and evaluation strategies like LLM-as-a-judge, offline vs online evaluation, and system-level metrics such as latency/cost/UX tradeoffs. Implement guardrails for input/output validation, schema enforcement (Pydantic), refusal logic, prompt injection defense, and safety frameworks like Guardrails.ai/OpenAI Guardrails. Finally, containerize and deploy end-to-end systems using AWS ECS/Fargate, SageMaker endpoints, API Gateway/ALB, with CI/CD and monitoring.

Project 1: Intelligent Document Portal (End-to-End RAG Deployment)

Build a production-grade Document Intelligence Portal that can ingest large document collections and answer questions with grounded, citation-backed outputs. You’ll implement the full RAG pipeline — upload → parse → chunk → embed → index → retrieve → generate — with advanced features like query rewriting, MMR retrieval, re-ranking, multi-document chat, and document comparison. You’ll also ship it with Redis caching (CAG), evaluation + guardrails, and full deployment on AWS ECS/Fargate with CI/CD + observability.

Project 2: Autonomous Report Generation System (Multi-Agent Deployment)

Build a complete multi-agent AI system that researches, analyses, and generates structured reports like a real AI analyst team. You’ll design multiple agents (Search, Reader, Analyst, Generator, Coordinator) and orchestrate them using LangGraph / CrewAI / AutoGen-style state workflows, with shared memory, tool-calling, and RAG grounding for citation-aware results. The final system includes human-in-the-loop checkpoints, safe termination logic, and a deployable backend architecture with FastAPI dispatcher, report preview UI, and complete traces/logs for monitoring.