Why this matters
Worked examples
Example 1: Convert an ad-hoc prompt into a managed template
Original (ad-hoc):
"Write a product description for the Acme Rocket. Keep it exciting and under 80 words."
Managed template:
{
"id": "product_description_v1",
"template": "You are a concise copywriter. Write a vivid, benefit-focused product description for {product_name} aimed at {audience}. Limit: {word_limit} words.",
"variables": {
"product_name": {"type":"string", "maxLen":60},
"audience": {"type":"enum", "values":["shoppers","engineers","executives"]},
"word_limit": {"type":"integer", "min":20, "max":120}
},
"metadata": {
"purpose":"E-commerce PDP copy",
"owner":"growth-team",
"success":"Clarity 4+/5 on rubric; CTR +3%"
}
}Benefits: reusable, testable, and measurable.
Example 2: Versioning with release notes and rollback
v1 - Baseline.
v2 - Added audience variable; expectation: +2 points on relevance (offline test).
Rollback rule: If online CTR drops >1% for 2 hours, revert to v1.When CTR dipped 1.4%, we rolled back automatically. Later, we fixed tone and shipped v3.
Example 3: Telemetry + evaluation loop
We log per-request: prompt_id, version, latency, tokens_in/out, rubric scores, and a binary success flag. Weekly, we compare v2 vs v1 on success rate and cost to decide whether to expand rollout.
Hands-on: minimal workflow
- Define the template, variables, and metadata.
- Create a small offline test set (10–50 examples).
- Run offline evaluation and record scores.
- Prepare a release note with hypothesis and rollback triggers.
- Ship to staging; sanity check logs and safety filters.
- Canary release to 5–10% traffic.
- Monitor telemetry; decide promote, iterate, or rollback.
- Document outcomes and learnings.
Exercises
Complete these tasks. You can compare with the solutions below each exercise card.
Exercise 1: Design a prompt template spec
Create a compact specification for a support-reply prompt. Include id, purpose, template, variables (with types and constraints), safety notes, and success criteria.
- Keep to ~150–250 words or an equivalent JSON/YAML block.
- Include at least one numeric constraint and one enum.
- Add a short note on how you would test it offline.
Show a possible structure
{
"id":"support_reply_v1",
"purpose":"Polite, accurate replies to customer emails",
"template":"You are a helpful agent. Summarize the user issue from: {email_snippet}. Provide a {tone} reply with up to {sentence_limit} sentences. Include one actionable next step.",
"variables":{
"email_snippet":{"type":"string", "maxLen":2000},
"tone":{"type":"enum", "values":["friendly","formal"]},
"sentence_limit":{"type":"integer", "min":2, "max":5}
},
"safety":{"pii_redaction":true, "avoid_promises":true},
"success":"Rubric: accuracy ≥4/5; tone ≥4/5; refusal if policy-violating."
}Exercise 2: Release plan and rollback
Write a short release plan for upgrading v1 to v2. Include hypothesis, metrics, offline baseline, canary size, monitoring window, and rollback trigger.
- Mention at least two metrics (quality and cost or latency).
- State an explicit rollback rule.
See a sample release plan
Hypothesis: v2 adds tone control; improves user satisfaction.
Offline baseline: Relevance +1.2 points; no cost increase.
Canary: 10% of traffic for 24h.
Metrics: Satisfaction CSAT, response latency, tokens per request.
Promote if CSAT +2% and latency within +5%.
Rollback if CSAT -1% for >2h or latency +15%.Common mistakes and self-check
- Mistake: No versioning. Fix: Tag each change and log notes.
- Mistake: Vague success criteria. Fix: Define clear rubrics and metrics before release.
- Mistake: Shipping straight to 100%. Fix: Use staging and canary.
- Mistake: Missing telemetry. Fix: Capture prompt id/version, latency, tokens, and key quality signals.
- Mistake: Hardcoding secrets in prompts. Fix: Store creds in secure config; reference variables only.
Self-check questions
- Can you locate the current production version and its release notes?
- Do you have at least one offline test set and one online metric?
- Is rollback clearly defined and tested?
Who this is for
Prompt Engineers, ML Engineers, and Product-minded researchers who need reliable, auditable prompt workflows from prototype to production.
Prerequisites
- Basic prompt design (system/user/assistant roles, variables).
- Familiarity with evaluation rubrics and A/B testing basics.
- Comfort with reading simple JSON/YAML specs.
Learning path
- Prompt design fundamentals
- Prompt management systems (this lesson)
- Automated evaluation and guardrails
- Experimentation and canary/A-B
- Observability and cost optimization
Practical projects
- Build a tiny prompt library: 3 templates, variables, and metadata; add a JSON file of release notes.
- Create an offline test set (20 examples) and a script that scores outputs with a simple rubric.
- Run a simulated canary by routing 10% of test inputs to v2 and compare metrics.
Mini challenge
Pick a prompt you currently use. Turn it into a managed template with variables and metadata, define a two-metric success criterion, and write a one-paragraph release note with rollback. Keep it under 10 minutes.
Next steps
Take the quick test to lock in the core ideas. Note: anyone can take the test; only logged-in users have their progress saved.