Learning Objective

Configure, optimize, and evaluate different LLMs while understanding their operational characteristics and limitations.

Learn how LLMs behave in real-world environments and understand the practical limitations that engineers must address while building enterprise AI applications. Explore inference parameters, hallucinations, model limitations, and strategies for working across multiple foundation models.

Topics:

• Hallucinations
• Knowledge Cut-off
• Sycophancy
• Verbosity Bias
• Temperature
• Top-p
• Top-k
• Frequency Penalty
• Presence Penalty
• Multi-Modal Models
• Multi-SDK Fluency
• OpenAI
• Claude
• Gemini

Learning Objective

Develop effective prompt design strategies that maximize the quality and reliability of LLM-generated responses.

Learn how to design effective prompts that improve model accuracy, consistency, and reasoning. Understand how prompt architecture influences LLM behaviour across different enterprise use cases.

Topics:

• Zero-shot Prompting
• One-shot Prompting
• Few-shot Prompting
• Role Prompting
• Persona Design
• System Prompt Architecture
• Prompt Chaining
• Sequential Prompt Pipelines

Hands-on Lab: Design prompt workflows for multiple enterprise business scenarios.

Learning Objective

Apply advanced reasoning techniques to significantly improve LLM performance on enterprise-grade tasks.

Move beyond basic prompting by implementing advanced reasoning strategies that enable LLMs to solve complex problems through structured thinking and iterative decision-making.

Topics:

• Chain-of-Thought (CoT)
• Tree-of-Thought (ToT)
• Self-Consistency
• ReAct Prompting
• Reasoning vs Acting
• Multi-step Problem Solving

Hands-on Lab: Implement and benchmark Chain-of-Thought, Tree-of-Thought, and Self-Consistency reasoning strategies.

Learning Objective

Develop production-ready LLM workflows capable of generating predictable, structured, and machine-readable outputs.

Learn how modern LLM applications interact with external systems using structured outputs and function calling. Build reliable AI workflows that integrate seamlessly with enterprise software and APIs.

Topics:

• JSON Mode
• JSON Schema Enforcement
• Typed Outputs
• Function Calling
• Schema Design
• Parallel Tool Calls
• Structured Response Contracts

Hands-on Lab: Build a structured output pipeline using JSON Schema, Pydantic validation, and typed Python objects.

Learning Objective

Design scalable prompt workflows capable of handling complex enterprise AI automation scenarios.

Learn how complex enterprise AI applications orchestrate multiple prompts into intelligent workflows. Design sequential prompt pipelines that solve sophisticated business problems through multiple reasoning stages.

Topics:

• Prompt Chaining Concepts
• Sequential Prompt Pipelines
• Workflow Design
• Multi-Step Prompt Strategies
• Context Preservation
• Enterprise Workflow Patterns

Hands-on Lab: Build an end-to-end multi-stage prompt workflow for a real-world enterprise use case.

Learning Objective

Confidently select the appropriate AI implementation strategy based on business requirements, scalability, and performance considerations.

Understand when Prompt Engineering alone is sufficient and when Retrieval-Augmented Generation (RAG) or Fine-Tuning provides a better solution. Learn how enterprise architects choose the right approach for different AI use cases.

Topics:

• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Fine-Tuning
• Decision Framework
• Cost vs Accuracy Trade-offs
• Enterprise Use Cases

Learning Objective

Optimize enterprise AI applications for cost, scalability, and performance without compromising response quality.

Enterprise AI systems must balance performance, latency, and operational cost. Learn techniques for optimizing LLM usage while intelligently routing requests across multiple models.

Topics:

• Token Counting
• Prompt Caching
• Cost Optimization
• Latency Optimization
• Model Routing
• Multi-Model Strategies

Hands-on Lab: Build an intelligent model router that automatically selects the most cost-effective LLM based on task complexity.

Learning Objective

Measure, validate, and continuously improve prompt quality across enterprise AI applications.

Reliable AI systems require continuous evaluation and testing. Learn how to measure prompt quality, benchmark model performance, and implement evaluation frameworks for production environments.

Topics:

• Prompt Evaluation
• Evaluation Metrics
• Benchmarking
• Automated Testing
• Quality Assurance
• Regression Testing

Hands-on Lab: Evaluate and compare prompt performance using standardized enterprise evaluation metrics.

Learning Objective

Manage the complete prompt lifecycle using industry-standard PromptOps practices suitable for enterprise AI teams.

Enterprise AI teams require governance and lifecycle management for prompts. Learn industry best practices for prompt versioning, collaboration, experimentation, and deployment using PromptOps principles.

Topics

• Prompt Version Control
• Prompt Management Systems
• PromptOps
• Experiment Tracking
• A/B Testing
• Governance & Documentation

Hands-on Lab: Implement prompt versioning, experimentation, and A/B testing using LangSmith.