Course Objective & Scope
A prompt that works well in a chat window often breaks when embedded in an automated workflow processing thousands of noisy, real-world inputs. In production automation, prompt engineering is an engineering discipline focused on context assembly, strict output schemas and repeatable evaluation.
In this hands-on course, participants use n8n to build dynamic prompt templates, apply few-shot selection, decompose complex tasks through prompt chaining, enforce JSON schemas and build automated evaluation workflows with test datasets and LLM-as-a-judge scoring.
Who Should Attend?
- Automation and software engineers building AI-powered workflows in n8n
- Teams improving accuracy in automated classification, extraction and content generation pipelines
- AI product owners looking to standardize, test and version production prompts
- Prerequisite: basic n8n skills and familiarity with generative AI concepts
Key Takeaways
- Construct parameterized prompt templates with clean separation of instructions and runtime data
- Replace fragile mega-prompts with router patterns and multi-step
prompt chaining - Guarantee valid downstream payloads using strict JSON schemas and auto-fixing parsers
- Run automated regression tests in n8n against golden datasets and scoring criteria
- Harden workflows against
prompt injection, context overflow and malformed edge cases
Curriculum
Automation-First Prompt Design
- Differences between interactive chat prompts and headless workflow prompts
- System instructions, XML/Markdown delimiters and safe dynamic
expressioninjection
Context Assembly and Dynamic Few-Shot Prompting
- Pre-processing CRM, database and API payloads to fit context windows cleanly
- Selecting relevant
few-shotexamples dynamically based on input similarity
Structured Outputs and Information Extraction
- Strict
JSON Schemadesign for multi-label classification and entity extraction - Handling malformed outputs with
Auto-fixing Output Parserand code validation
Prompt Chaining and Routing Patterns
- Decomposing complex tasks into sequential analysis, drafting and critique chains
- Implementing intent routers and evaluator-optimizer loops on the n8n canvas
Automated Evaluation, Versioning and Guardrails
- Building n8n evaluation pipelines over golden datasets with
LLM-as-a-judgemetrics - Defending against
prompt injection, externalizing prompt versions and tuning latency/cost
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