Context Engineering Fundamentals
Frustrated by shallow AI answers? Learn to think like a context engineer—design conversation flows, retrieval pipelines, and multi-agent systems with ethical, no-code tools in Flowise backed by modern AI.
In partnership with
Overview
Are you frustrated when AI tools give shallow, incomplete, or hallucinated answers - even though you're using powerful models?
In most real-world cases, the problem isn't the model. It's how information flows into the model: what context you send, how you structure it, how you retrieve it, and how your prompts instruct the system to use it.
This foundational course teaches you to think like a context engineer for AI systems. You'll learn how to design end-to-end context systems - from conversation flow and retrieval pipelines to multi-agent architectures and ethical AI considerations - using visual, no-code tools in Flowise backed by modern AI infrastructure.
Skills you'll gain
Context ArchitectureAI Framework LandscapeMulti-Agent PatternsEthical AI Practices Metadata-Rich
What you'll learn
Analyze and debug how AI agents use context by examining conversation state, token and context-window limits, and end-to-end context flow in Flowise and LangSmith - identifying where and why responses fail.
Design and implement effective retrieval pipelines by transforming user queries, applying multi-query strategies, chunking and embedding documents, and using vector databases, metadata filters, and rerankers to deliver the most relevant context to the model.
Evaluate and compare AI frameworks (CrewAI, LangGraph, Autogen) and hyperscaler AI services, and design multi-agent patterns for complex workflows involving research, summarization, and decision-making.
Implement ethical AI practices by identifying context vulnerabilities, designing appropriate guardrails, and applying mitigation strategies for context failures in production systems.
Who Should Attend
Prerequisites
This course is designed for IT professionals, developers, and AI enthusiasts who want to improve the quality of AI outputs. Ideal learners have some experience with AI/ML concepts, programming, or systems development and are interested in building context-aware, ethical AI solutions using no-code tools.
Learners should have basic knowledge of SQL and Python, an understanding of AI and ML fundamentals, and experience in IT systems development. Familiarity with generative AI concepts such as RAG, AI agents, and vector databases, as well as experience working with APIs and data structures, is also recommended.
Chapters
Explore a structured set of chapters designed to build your skills step by step, with practical examples and hands-on applications.
Meet your instructors
Manas Dasgupta
View my channelFrequently Asked Questions
How much do the courses at Starweaver cost?
We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.
Do you offer any certifications upon completion of a course at Starweaver?
Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.
Does Starweaver offer any free courses or trials?
Related Modules
%252520Starweaver%252520Strategic%252520Leadership%252520Visionary%252520Skills%252520for%252520Success.jpg&w=640&q=75)
.png&w=640&q=75)
NEW
6 hours of video content
Downloadable resources
Certificate of completion
Desktop and Mobile access
Manas Dasgupta