What to know before you read.
- Choose a narrow task with representative inputs and a measurable finish line.
- Ground factual outputs in supplied sources and test unsupported claims explicitly.
- Show evaluation, failure handling and cost—not only a polished chat interface.
Pick a task, not a chatbot
Define one user, source of truth and repeatable outcome. Examples include answering policy questions with citations, extracting structured fields from documents or drafting replies from approved guidance. Avoid a general assistant because its boundaries and success criteria are too vague.
Design the evidence path
Collect a small permitted document set, preserve source metadata and split content into meaningful chunks. Retrieve candidate evidence and require outputs to reference it. Define what the application should do when evidence is missing or conflicting.
Create an output contract
Specify required fields, tone, length, citations and uncertainty behaviour. Validate structured output before another system uses it. Separate system rules from user content and treat retrieved text as untrusted data to reduce prompt-injection risk.
Build an evaluation set
Create ordinary, edge, ambiguous and unsupported examples before tuning. Score task completion, citation support, refusal quality, format validity, latency and cost. Keep failed examples because they reveal more about engineering judgement than selected successes.
- Representative test questions
- Expected evidence
- Task-specific rubric
- Failure categories
- Versioned results
Publish the reasoning
Document architecture, data permission, prompt versions, evaluation results, limitations and next controls. The Generative AI Certificate of Specialisation gives you a structured 30-hour path from model behaviour to a grounded capstone.
Questions readers often ask.
What is a good beginner generative AI project?+
A cited document assistant or structured extraction workflow has clear evidence, constraints and evaluation.
Do I need to train a model?+
No. Most beginner projects should use an existing model and focus on context, workflow, evaluation and controls.
How do I evaluate a generative AI project?+
Use representative cases and score grounding, task completion, format, safety, latency and cost.
