Module 1 : Introduction to AI - Concepts, Working & Types
● Core AI Concepts : Understand AI, machine learning and generative AI from a corporate systems perspective. ● How AI Systems Work : Input, tokenization, model inference to output generation explained clearly. ● Types of AI : Predictive, generative and rule-based AI systems in enterprise applications. ● Enterprise Use Cases : How AI supports analytics, automation, operations and decision-making.
Module 2 : LLM Architecture & Behavior
● Tokenization & Embeddings : How data is broken down and represented internally by AI systems. ● Probabilistic Output Generation : How models predict and generate responses. ● Context Windows : Input limits and how they affect output completeness and accuracy. ● Output Variability : Why small changes in prompts can significantly alter outputs.
Module 3 : Prompting for Controlled & Structured Outputs
● Prompt Engineering as Control : Using prompts as a mechanism to guide system behavior. ● Structured Prompt Framework : Task, Context, Constraints, Format and Output definition. ● Advanced Prompting Techniques : Role prompting, step-wise prompting and structured output formats. ● Hands‑On Lab : Design prompts for structured reports and analytical workflows.
Module 4 : Output Control & Structured Generation
● Forcing Output Structure : Generating tables, lists, structured formats and standardized outputs. ● Controlling Output Style : Managing tone, reasoning depth and detail levels. ● Consistency Techniques : Reducing variation and maintaining repeatability. ● Practical Exercise : Convert unstructured outputs into controlled, production-ready formats.
Module 5 : Output Evaluation & Reliability Frameworks
● Evaluation Criteria : Accuracy, completeness, consistency and logical validity. ● Failure Modes : Hallucinations, ambiguity, missing assumptions and inconsistency. ● Validation Techniques : Iterative prompting, cross-checking and structured verification. ● Hands‑On Lab : Refine and improve unreliable outputs using evaluation frameworks.
Module 6 : AI Integration Concepts - Light Code & System Thinking
● AI as a System Component : Understanding how AI fits into enterprise architecture. ● Input–Process–Output Pipelines : Designing structured workflows using AI steps. ● Prompt as Interface : Treating prompts as programmable inputs. ● Light Coding Concepts : Functions, structured logic and simple automation ideas. ● Hands‑On Lab : Walk through a simple AI-driven workflow using structured logic.
Module 7 : Workflow Design, Automation & Scaling AI
● Workflow Patterns : Single-step vs multi-step AI-driven workflows. ● Prompt Chaining : Linking multiple prompts into structured processes. ● Automation Thinking : Reducing manual work using repeatable logic. ● Hands‑On Lab : Design a scalable AI workflow for a business use case.
Module 8 : Responsible AI, Governance & Enterprise Risk
● Risk Identification : Bias, hallucinations, misuse and operational risks. ● Data Privacy & Security : Handling sensitive and confidential information safely. ● Governance Frameworks : Human-in-the-loop, approvals and accountability. ● Enterprise Readiness : Compliance, auditability and responsible system deployment.
Module 9 : Capstone - AI System Design for Business Use (Optional)
● Use Case Selection : Identify scalable and high-value applications of AI. ● Workflow & Prompt Design : Build structured systems for real-world tasks. ● Validation & Control Layers : Integrate reliability and governance checks. ● Final Output : Develop a complete AI-enabled workflow ready for implementation.