Frequently Asked Questions
Common Questions About Going Beyond Prompt Engineering
Everything you need to know before registering.
What can prompt engineering not solve that context engineering can? +
Prompt engineering cannot solve: multi-agent coordination (how agents communicate and share context), memory persistence across sessions, context window overflow in long interactions, systematic hallucination prevention, production safeguards against adversarial inputs, or system observability for debugging. Context engineering addresses all of these through architectural design rather than prompt optimization.
When should I use prompt engineering versus context engineering? +
Use prompt engineering to optimize the quality of a single LLM call: improving instruction clarity, output format, or few-shot examples for a specific task. Use context engineering when you need to build a system with multiple LLM calls that coordinate, maintain state, and work reliably under real-world conditions. Production AI systems need both: context engineering for architecture and prompt engineering within that architecture.
How much of production AI failure is caused by bad prompts versus bad architecture? +
The majority of production AI failures are architectural rather than prompt-related. Context overflow, coordination failures between agents, hallucination without citation grounding, prompt injection vulnerabilities, and inability to debug system behavior are all architectural problems that better prompts cannot fix. Once the architecture is sound, prompt engineering has its appropriate role optimizing individual component quality.
What is the first context engineering concept I should add to an existing prompt-only system? +
The first and highest-impact addition to a prompt-only system is semantic blueprints: replacing unstructured prompts with explicitly structured agent specifications that define role, goal, knowledge boundaries, and output format. This single change makes the system significantly more predictable without requiring a complete architectural overhaul.
Is prompt engineering still valuable if I implement context engineering? +
Yes. Context engineering and prompt engineering are complementary. Context engineering provides the architectural structure (how agents are organized and how context is managed). Within that structure, prompt engineering optimizes the quality of individual agent instructions. The semantic blueprint is itself a prompt: context engineering structures it, and prompt engineering optimizes the content within that structure.
What does the transition from prompt engineering to context engineering look like in practice? +
The transition is gradual and component-by-component. You start by adding semantic blueprints to existing prompts, then introduce MCP for any agent coordination, then add the Glass-Box logging layer for observability, then build the RAG pipeline for knowledge grounding. The workshop covers this incremental migration path so you can improve production AI systems without a big-bang rewrite.
