Module 1: AI Has Changed Engineering Decisions

Start Understand what changed, what did not, and why AI systems require a different engineering mindset.

• What modern generative AI systems actually do
• Why AI output is probabilistic rather than deterministic
• Models, inference, tokens, context, and hallucination
• Why an impressive prototype can still be a poor production system
• The role of conventional software around an AI model
• Recognizing where ambiguity, inconsistency, and hidden assumptions can appear
• When AI should not be used

Module 2: Choosing the Right AI Opportunity and Architecture

Learn how to decide what belongs in AI and what architecture best fits the task

• Automation vs augmentation
• Conventional software vs AI-assisted workflows
• Frontier vs smaller/local models
• Hosted vs private deployment
• Capability, quality, cost, latency, privacy, and data-handling trade-offs
• Using different models for different tasks
• Comparing model quality, latency, consistency, and cost
• Model routing and escalation
• Using the least expensive execution path that still meets the quality requirement
• Recognizing when conventional code is cheaper and more reliable than an LLM
• Designing a practical first pilot

Module 3: Giving AI Enterprise Knowledge

Give AI access to current, private, and authoritative information without expecting the base model to know everything.

• Embeddings and semantic search
• Retrieval-Augmented Generation (RAG)
• Context-window trade-offs
• Chunking and retrieval quality
• RAG vs fine-tuning
• When authoritative data should come from an API instead of a document
• Grounding responses in enterprise information
• Evaluating whether retrieval actually improved the result

Module 4: From Answers to Actions

Move from an AI system that only talks to one that can safely interact with real systems.

• Structured output
• Function and tool calling
• Connecting models to APIs
• Tool schemas and validation
• Errors, retries, and partial failure
• Idempotency and duplicate actions
• Human approval for consequential actions
• Agent loops and when they are appropriate
• When an agent is unnecessary
• Model Context Protocol (MCP)
• MCP clients, servers, tools, and resources
• Direct API integration vs MCP
• Designing boundaries around what an AI system is allowed to do

Module 5: Can You Trust It

Design a quality control layer around a probabilistic component

• Deterministic validation
• Evaluation datasets
• Expected vs acceptable outcomes
• LLM-as-a-judge
• Designing evaluation rubrics
• Why the evaluator can also be wrong
• Retry vs escalation
• Human-in-the-loop review
• Using stronger models selectively when lower-cost models are not good enough
• Measuring the quality/cost trade-off of different routing strategies
• Measuring quality, latency, and cost over time

Module 6: Security and Production Reality

Understand what must change before an AI prototype can be trusted in front of real users

• Prompt injection and malicious retrieved content
• Data leakage and excessive permissions
• Tool abuse and uncontrolled actions
• Model and service failures
• Runaway automation and runaway cost
• Observability and logging
• Prompt and model versioning
• Latency and cost monitoring

●       Graceful degradation

●       Human review and governance boundaries

●       Applying these controls to a realistic enterprise AI implementation

This artificial intelligence course is designed for professionals involved in selecting, designing, approving, or implementing AI-enabled systems, including:

• Software engineers and technical leads
• Software and solution architects
• Engineering managers and directors
• Platform and DevOps professionals
• Technical consultants and solution engineers
• Product and technical product leaders
• Technology executives who need enough technical depth to make sound implementation decisions

Prerequisites: Programming experience is helpful but not required.

Follow On: This one-day course is designed as a practical precursor to deeper hands-on AI engineering training. Participants who need to implement these patterns in code can continue into Cprime's AI Engineering: Build and Ship Production Ready AI Systems, a 14-hour live program covering RAG, tool use, agents, MCP, model routing, evaluation, security, cost control, and production reliability in substantially greater depth.

• Identify business problems where AI adds value and recognize problems better solved with conventional software.
• Compare frontier, smaller, local, and private model approaches.
• Choose among prompting, RAG, tools, agents, and other common AI application patterns.
• Understand how AI systems connect to enterprise knowledge and operational systems.
• Evaluate output using deterministic checks, AI-based evaluation, escalation, and human review.
• Understand the practical role of MCP in connecting AI systems to tools and data.
• Evaluate and control cost alongside latency, privacy, security, quality, and reliability.
• Recognize common production risks including hallucination, prompt injection, data leakage, and uncontrolled automation.
• Create a practical implementation plan for an enterprise AI use case.