Context Engineering Foundation is a two-day, hands-on course for engineers and architects. It teaches them to design, manage, and evolve context as a first-class system in modern LLM-powered applications. The course moves beyond ad-hoc prompt engineering. Day one opens by defining what context engineering actually covers. It then moves into tokenization and the economics of context. Attendees learn to reason about cost, latency, and accuracy tradeoffs using a context budget mindset. An instruction hierarchy module follows, building stable, layered system prompts that resist leakage and prompt injection.
Day one closes with a module on representing knowledge, comparing structured data, few-shot examples, and embeddings as ways to ground a model’s responses. Day two turns to retrieval-augmented context, where students build RAG pipelines and learn to recognize common failure modes before they reach production. A memory and state module follows, contrasting short-term and long-term memory strategies and showing when accumulated state does more harm than good.
A tool context module then covers designing clear model-tool boundaries and schemas. The course closes with a module on evaluating and evolving context designs using regression testing and quantitative metrics. Through lectures, demos, and labs, attendees gain repeatable patterns for building scalable, evaluable, and maintainable context pipelines. These pipelines support real-world AI systems and agent-like workflows.
Who Should Attend
ML/AI Developers, Software & Data Engineers, Solution/Software Architects, Technical Managers
What Attendees Will Learn
Upon completing Context Engineering Foundation, participants will be able to:
- Design context as a structured system composed of instructions, data, memory, tools, and state
- Analyze context window constraints and make informed tradeoffs between cost, latency, accuracy, and reliability using a “context budget” mindset
- Create stable, layered instruction hierarchies that prevent conflicts, leakage, and prompt-injection vulnerabilities
- Select and apply appropriate context representation strategies, including structured data, few-shot examples, embeddings, and inline references
- Build and troubleshoot retrieval-augmented context pipelines, identifying and correcting common RAG failure modes
- Implement effective short-term and long-term memory strategies while avoiding unnecessary state accumulation and context drift
- Define clear model–tool boundaries and design tool schemas that enable reliable, agent-like workflows
- Evaluate, version, and iteratively improve context designs using qualitative analysis, quantitative metrics, and regression testing
Prerequisites
Attendees will need basic Python skills and basic familiarity with LLM concepts.