AI Foundations, Prompt Engineering & Context Engineering

AI Foundations, Prompt Engineering & Context Engineering

(2-Day Intensive)

Understand AI clearly, control it deliberately, and design reliable AI systems without being a machine learning engineer.

AI has crossed a threshold: it is no longer confined to research labs or engineering teams. It writes, summarizes, plans, reasons, retrieves knowledge, triggers actions, and increasingly acts on behalf of people. Yet most failures with AI today do not come from weak models—they come from poor understanding, unclear intent, weak context design, and misplaced trust.

This course is designed to give participants a clean, practical mental model of AI—what it is, how it works, where it fails, and how to use it responsibly and effectively. It avoids mathematical formalism and academic abstraction, focusing instead on how AI behaves in real systems, why outputs change, how prompts and context shape outcomes, and how agentic systems introduce new risks and opportunities.

Taught by an instructor with over 30 years of industry experience, this program emphasizes industry-demanded skills, production realities, and decision-making frameworks that apply whether you are technical or not. The goal is not to turn participants into data scientists, but into AI-literate professionals who can design, evaluate, and govern AI systems competently.

Learning Outcomes

By the end of this course, participants will be able to:

• Explain what AI is in plain language and distinguish it from automation and traditional software.
• Understand how modern AI systems—and especially large language models—work at a conceptual and systems level.
• Classify AI systems by capability, autonomy, learning style, and risk.
• Identify core AI limitations including non-determinism, black-box behavior, hallucinations, and emergent behaviors.
• Design effective prompts using structured, repeatable prompt engineering patterns.
• Engineer context, not just prompts, to reliably guide AI behavior across tasks and workflows.
• Understand and evaluate agentic AI systems and autonomous workflows.
• Recognize ethical, legal, operational, and organizational risks in AI deployment.
• Participate meaningfully in AI projects, procurement decisions, governance discussions, and system reviews.
• Apply evaluation and oversight techniques appropriate for non-deterministic AI systems.

Prerequisites

• General computer literacy and familiarity with digital tools.
• No programming, data science, or machine learning background required.
• Willingness to think clearly about language, instructions, and decision-making.
• Curiosity about how AI systems behave in real-world environments.

Detailed Training Outline

1. What AI Really Is (and Why the Term Is So Misused)

• The problem with the word “AI”
  • Why AI is not a single thing
  • Why marketing language creates dangerous misunderstandings
• AI vs traditional software
  • Deterministic systems vs probabilistic systems
  • Rules, logic, workflows, and automation
• Core definition of AI (practical, not philosophical)
  • Goal-directed behavior under uncertainty
  • Pattern learning vs explicit instruction
• Common categories of AI use
  • Prediction and classification
  • Generation (text, images, code)
  • Decision support and optimization
  • Planning and task execution
• What AI does not do
  • Understanding vs pattern matching
  • Intent vs imitation
  • Reasoning vs structured correlation

2. How AI Systems Work (Without Math or Code)

• The basic AI system pipeline
  • Data → model → objective → training → inference
• Learning from examples
  • Why AI learns patterns, not rules
  • The role of probability in AI behavior
• Training vs using a model
  • Why training happens once, but inference happens constantly
• Why AI outputs change
  • Randomness and probability
  • Context sensitivity
  • Environmental inputs and system state
• Why AI systems are not predictable like software
  • Statistical behavior vs deterministic execution

3. AI Classification: Understanding the Landscape

• Classification by learning approach
  • Supervised learning
  • Unsupervised learning
  • Self-supervised learning
  • Reinforcement learning
• Classification by capability
  • Narrow task-specific AI
  • General-purpose models
  • Multi-modal systems
• Classification by autonomy
  • Assistive systems
  • Semi-autonomous systems
  • Fully agentic systems
• Classification by risk and impact
  • Low-risk productivity use
  • Medium-risk decision support
  • High-risk autonomous or consequential systems
• Why classification matters for governance, cost, and trust

4. Large Language Models (LLMs) Explained Clearly

• What an LLM actually is
  • Text prediction at scale
  • Tokens, not words
• How LLMs “generate” responses
  • Probability distributions over possible next tokens
  • Why fluent language does not equal correctness
• Training lifecycle
  • Pretraining on large corpora
  • Instruction tuning
  • Preference and alignment tuning
• Why LLMs sound confident even when wrong
  • Fluency bias
  • Lack of grounding
• Practical limits of LLMs
  • Context window constraints
  • Knowledge cutoffs
  • Sensitivity to phrasing and order

5. Core Problems and Concerns in AI Systems

• Non-determinism
  • Why the same input does not always produce the same output
  • Reproducibility challenges
• Black-box behavior
  • Why explanations are approximations
  • The danger of over-interpreting justifications
• Hallucinations
  • Why AI fabricates information
  • Conditions that increase hallucination risk
• Emergent behavior
  • Unexpected capabilities
  • Sudden performance jumps
• Over-reliance and automation bias
  • Human trust failures
  • Deskilling risks
• Security risks
  • Prompt injection
  • Data leakage
  • Tool misuse in agentic systems

6. Prompt Engineering Foundations (Non-Technical and Practical)

• What prompt engineering actually is
  • Instruction design, not magic words
• Anatomy of a good prompt
  • Task definition
  • Role specification
  • Constraints and boundaries
  • Output format control
• Prompt clarity and ambiguity management
  • Assumptions
  • Definitions
  • Scope limits
• Prompt patterns that work reliably
  • Step-by-step decomposition
  • Structured output prompts
  • Checklist-based shows of work
• Prompt anti-patterns
  • Overloaded prompts
  • Conflicting instructions
  • Implicit expectations
• Prompt lifecycle management
  • Versioning
  • Testing
  • Iterative refinement

7. Context Engineering (Beyond Prompts)

• Why prompts alone are not enough
  • The difference between instruction and environment
• What “context” means in AI systems
  • System messages
  • Retrieved knowledge
  • Tool outputs
  • Conversation history
  • Hidden operational constraints
• Context vs memory
  • Short-term context
  • Long-term context
• Designing effective context
  • Relevance filtering
  • Ordering and prioritization
  • Noise reduction
• Context failure modes
  • Context overload
  • Conflicting signals
  • Stale or misleading information
• Context engineering for non-technical roles
  • Business rules as context
  • Policies and guardrails
  • Brand, tone, and compliance context

8. Retrieval, Grounding, and Knowledge Injection

• Why models should not be trusted alone
• Retrieval-augmented generation (conceptually)
  • External knowledge sources
  • Grounding responses in facts
• Embeddings and similarity (high-level intuition)
• Designing trustworthy knowledge flows
  • Source selection
  • Update frequency
  • Attribution and traceability
• Risks of poor grounding
  • Confident misinformation
  • Outdated or biased sources

9. Agentic AI and Autonomous Systems

• What agentic AI means in practice
  • Goals, plans, actions, feedback
• Difference between chatbots and agents
• Single-agent vs multi-agent systems
• Tool-using agents
  • APIs, databases, browsers, workflows
• Autonomy spectrum
  • Suggestion-only systems
  • Human-approved execution
  • Fully autonomous execution
• Risks unique to agentic AI
  • Runaway behavior
  • Compounding errors
  • Action without understanding
• Guardrails for agentic systems
  • Permissions
  • Limits
  • Human-in-the-loop controls

10. MCP and AI System Connectivity (Conceptual Understanding)

• The integration problem in AI systems
• Why tools, data, and models need standardized interfaces
• Conceptual overview of MCP
  • Context sharing
  • Tool access
  • Separation of responsibilities
• When MCP-style approaches make sense
• Security and governance implications
  • Trust boundaries
  • Auditability
  • Access control

11. Evaluation and Quality Control for AI Systems

• Why traditional testing does not work well for AI
• Defining “good enough” behavior
• Evaluation dimensions
  • Accuracy
  • Usefulness
  • Safety
  • Consistency
• Human vs automated evaluation
• Monitoring AI in real use
  • Drift
  • Degradation
  • Misuse
• Incident response for AI failures

12. Ethics, Responsibility, and Governance

• Why ethics is an operational concern, not philosophy
• Core ethical principles translated into practice
  • Fairness
  • Accountability
  • Transparency
  • Privacy
• Organizational responsibility
  • Who owns AI decisions
  • Who approves deployment
• Bias and representational harm
• Legal and compliance awareness (high-level)
• Building trust without over-promising

13. Designing AI Systems for Real Organizations

• Build vs buy decisions
• Choosing the right level of AI sophistication
• Cost, risk, and value tradeoffs
• Cross-functional collaboration
  • Product
  • Legal
  • Security
  • Operations
• Change management and AI adoption

14. End-to-End AI System Thinking

• Framing the problem correctly
• Selecting the right AI approach
• Designing prompts, context, tools, and oversight together
• Embedding safety and evaluation from day one
• Knowing when not to use AI