Context Engineering – Data Track

Total Duration: ~46 Hours
Mode: Instructor-led Training + Hands-on Labs + Capstone Project

Course Overview

This course is designed to enable participants to design, build, and operationalize context-aware AI systems for enterprise use cases. It focuses on the end-to-end lifecycle of context in AI systems — context creation, retrieval, orchestration, pipelines, enterprise integration, and governance. By the end of the course, participants will be able to build production-grade RAG and context pipelines that are scalable, secure, and high-quality.

Course Objectives

• Understand and implement Context Engineering (CE) in AI systems
• Design RAG architectures (basic to advanced and agentic RAG)
• Build document intelligence pipelines (ingestion → chunk → embed → store)
• Implement retrieval optimization techniques (MMR, re-ranking, query rewriting)
• Orchestrate dynamic context with prompts and metadata-aware injection
• Build embedding-driven ETL pipelines with monitoring and drift detection
• Integrate Context Engineering with enterprise platforms (Databricks, Snowflake)
• Apply governance, PII masking, and security practices
• Deliver production-ready context-aware AI systems

Target Audience

Data Engineers · AI/ML Engineers · GenAI Engineers · Solution Architects

Detailed Module Outline

Module 1: Foundations of Context Engineering | 6 hrs

Topics: Introduction to Context Engineering · Role of CE in LLM systems · Context vs Data vs Knowledge · Context lifecycle in AI pipelines · CE in the Data Engineering ecosystem

Hands-On: Analyze real-world LLM systems

Learning Outcome: Understand how context drives AI system accuracy and reliability

Module 2: LLMs, Embeddings & Retrieval Fundamentals | 6 hrs

Topics: Context windows and token limits · Embeddings (dense, sparse, hybrid) · Similarity search techniques · Embedding drift and challenges · Vector databases (FAISS, Chroma, Pinecone)

Hands-On: Build a FAISS-based similarity search system

Learning Outcome: Implement embedding-based retrieval systems

Module 3: Document Intelligence & Context Preprocessing | 6 hrs

Topics: Document ingestion (PDF, OCR, images, tables) · Chunking strategies (recursive, semantic) · Metadata engineering · Context structuring techniques

Hands-On: Build a document → chunk → embedding pipeline

Learning Outcome: Prepare high-quality context for AI systems

Module 4: Retrieval-Augmented Generation (RAG) Engineering | 6 hrs

Topics: RAG architectures (basic, hybrid, graph, agentic) · Query transformation (HyDE, rewriting, expansion) · Retrieval optimization (MMR, re-ranking) · Context selection strategies

Hands-On: Build a RAG pipeline with query rewriting and MMR

Learning Outcome: Build production-ready RAG systems

Module 5: Prompt & Context Orchestration | 6 hrs

Topics: Prompt engineering fundamentals · Few-shot and template design · Dynamic context assembly · Metadata-aware prompt injection · Context window optimization

Hands-On: Build a dynamic context orchestrator

Learning Outcome: Design intelligent prompt and context pipelines

Module 6: Context-Aware Data Pipelines | 6 hrs

Topics: Embedding-driven ETL pipelines · Incremental indexing and refresh · Context quality metrics (precision/recall) · Drift detection and monitoring · Observability for RAG systems

Hands-On: Build an Airflow pipeline for embedding updates

Learning Outcome: Operationalize context pipelines

Module 7: CE for Enterprise Platforms | 6 hrs

Topics: Databricks Mosaic AI Vector Search · Unity Catalog functions as tools · Model serving for embeddings · Snowflake Cortex Search · AI functions and document parsing

Hands-On: Explore enterprise integrations

Learning Outcome: Integrate CE with enterprise platforms

Module 8: Governance, Security & Compliance | 6 hrs

Topics: Context lineage and governance · PII detection and masking · Prompt injection attacks · Hallucination risks · Responsible AI practices

Hands-On: Implement PII masking before embedding

Learning Outcome: Build secure and compliant AI systems

Module 9: Capstone Projects | 4 hrs

Topics: Enterprise RAG Search Engine · Context-Aware Data Quality Assistant

Hands-On: Build an end-to-end CE pipeline

Learning Outcome: Demonstrate real-world CE system implementation

Final Outcome Statement

After completing this course, participants will be able to design and implement context-driven AI systems, build RAG pipelines with optimized retrieval and orchestration, manage context lifecycle in production environments, integrate AI systems with enterprise platforms, and ensure security, governance, and observability.