Course Objective & Scope
Trying to solve a complex business process with a single giant prompt leads to brittle, unpredictable outputs. This course shows how to decompose complex work into reliable LLM workflow patterns—prompt chaining, routing, parallel execution, orchestrator-workers and evaluator-optimizer loops—enriched with internal knowledge retrieval and bounded tool calling.
Rather than unconstrained autonomous agents, participants build traceable, predictable intelligent workflows and evaluate each step against a test set to produce a reusable architecture template.
Who Should Attend?
- Automation architects, technical business analysts and solution builders
- Software and data teams building internal LLM-powered products and workflows
- Specialists building flows in n8n, LangGraph, Power Platform or custom APIs
- Prerequisite: basic familiarity with APIs/JSON and prompt engineering
Key Takeaways
- Break monolithic prompts into verifiable multi-step prompt chains
- Apply router and parallelisation patterns to improve quality and reduce latency
- Use tool/function calling so LLMs fetch context safely from enterprise systems
- Implement evaluator-optimizer loops that check outputs against explicit rubrics
- Choose between structured workflows and autonomous agents based on risk and cost
Curriculum
LLM Workflow Patterns vs Autonomous Agents
- Prompt chaining, routing, parallelisation and orchestrator-worker patterns
- When to use a fixed workflow versus an open-ended agent loop
Prompt Chaining and State Management
- Passing structured state between steps with intermediate validation gates
- Preventing context bloat through selective summarisation and filtering
Context Enrichment and Tool Calling
- Fetching relevant records from CRM, ERP or knowledge bases before inference
- Defining function/tool schemas and validating parameters
Evaluator-Optimizer Loops and Quality Gates
- Separating generator and critic/judge steps
- Setting retry limits and human escalation rules
Evaluation and Production Readiness
- Measuring step-by-step accuracy against a golden test dataset
- Regression checks against model updates and prompt drift
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