Course Objective & Scope
In enterprise AI, most failures are no longer prompt wording problems—they are context problems: the model either lacks critical domain knowledge or is overwhelmed by irrelevant, conflicting documents. Context Engineering is the discipline of designing, curating, structuring, and budgeting the complete information payload inside a model's context window.
Participants explore how attention degrades across large context windows (context rot and lost-in-the-middle effects), how to maximize signal-to-noise ratio, and how to assemble static instructions, retrieved documents, memory, and examples into high-performing context packages.
Who Should Attend?
- Business analysts, AI product managers, and domain specialists designing internal assistants and AI workflows
- Teams working with complex policies, multi-document cases, customer histories, or knowledge bases
- Practitioners looking to advance from single-prompt writing to systemic Context Engineering
- Foundational familiarity with LLM prompting is recommended
Key Takeaways
- Transition from wording-focused prompting to holistic Context Engineering across instructions, knowledge, and state
- Mitigate context rot, attention dilution, and lost-in-the-middle degradation in long context windows
- Define and budget the Minimum Viable Context (MVC) required for specific corporate tasks
- Format unstructured documents, tables, and metadata using XML/Markdown structures that optimize model recall
- Design a repeatable Context Collection and Bounding Model for departmental assistants and workflows
Curriculum
From Prompt Wording to Holistic Context Engineering
- Deconstructing the context payload: system rules, domain documents, few-shot examples, memory, and tool outputs
- The fallacy of dumping everything into a 1M-token window: latency, cost, and accuracy degradation (context rot)
- Defining Minimum Viable Context (MVC): selecting high-signal information while excluding distracting noise
Context Window Mechanics and Attention Optimization
- Understanding Lost-in-the-Middle phenomena and how attention distributes across long inputs
- Context poisoning and distraction: how outdated or tangential snippets derail model reasoning
- Strategic placement: leveraging primacy and recency positions for critical instructions and queries
Structuring, Tagging, and Compacting Context
- Enriching document chunks with explicit metadata (source title, effective date, authority level)
- Bounding context blocks cleanly using XML tags (context and source_doc blocks) and Markdown hierarchies
- Context compaction and rolling state summaries to prevent multi-turn conversation degradation
Enterprise Context Architecture Workshop
- Separating static institutional context (cached rules/glossaries) from dynamic case-level context
- Designing conflict-resolution rules when retrieved documents contain differing dates or policies
- Workshop: Building a complete Context Collection and Bounding Model for a target enterprise workflow
Other Courses in This Category
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Practical course on curating, structuring, and governing organizational wikis, SOPs, and document repositories so AI assistants and RAG workflows retrieve accurate, conflict-free context.
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Intermediate course on embedding classic analytical and consulting frameworks—SWOT, PESTEL, Root Cause Analysis, Red-Teaming, and Decision Matrices—into structured prompts for rigorous analysis.
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Practical course on replacing 'vibe checks' with systematic prompt evaluations—building golden test datasets, grading rubrics, edge-case suites, and LLM-as-a-Judge scoring workflows.
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Practical course on designing, organizing, versioning, and governing a shared enterprise prompt library so teams stop reinventing the wheel and scale proven AI workflows.
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Specialized security training on defending corporate AI assistants and workflows against direct and indirect prompt injection, jailbreaks, system prompt leakage, and untrusted inputs.
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