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Tutorial 10: From Prototype to Product

  • Contributor
  • 4 days ago
  • 2 min read

LLM demos are easy. Production is hard. The leap requires quality, reliability, and discipline.

Step 1: The Gap (15 min)

Demo:

  • One happy-path query

  • One model

  • One user (you)

Production:

  • Thousands of edge-case queries

  • Mix of models / fallbacks

  • Many users

  • Adversarial input

  • Real cost

  • Compliance

  • SLAs

The gap is large.

Step 2: Quality Bar (15 min)

Define:

  • What's correct?

  • What's the acceptable error rate?

  • What's safe vs unsafe?

For example: customer support bot:

  • 95% accuracy on FAQs

  • 0% hallucinations on prices / policies

  • 99% appropriate tone

Concrete bars > "good quality."

Step 3: Eval Discipline (15 min)

(Tutorial 6.)

Continuous eval:

  • Test set growth

  • Pre-deploy gates

  • Production sampling

  • User feedback integration

Without: cannot ship safely.

Step 4: Failure Modes (15 min)

Handle:

  • LLM provider down

  • Rate limited

  • Invalid output

  • Hallucinated facts

  • Off-topic responses

  • Inappropriate content

Each: a code path.

try:
    result = call_llm(...)
    if not valid(result):
        return fallback_response()
except RateLimit:
    return cached_response() or "try again later"

Step 5: Rollout Strategy (15 min)

Don't ship 100% day one:

  • 1% internal users

  • 10% beta

  • 50% gradual rollout

  • 100% with rollback ready

Watch metrics; SLOs.

Feature flag based:

if flag('new-llm-flow', user): return new_flow(...)
return old_flow(...)

Step 6: Human in Loop (15 min)

For high-stakes:

  • Suggestions to humans (copilot)

  • Confidence threshold; route low to human

  • Pre-publish review

"Tools for humans" > "fully autonomous"

For most apps: hybrid better.

Step 7: Cost Discipline (15 min)

Set per-user / per-tenant budgets.

if monthly_usage[user] > BUDGET:
    return "Out of allowance"

Without limits: one user can burn $$$.

Especially: prompt injection or runaway agents.

Step 8: Privacy + Compliance (15 min)

LLM calls = data leaving your infra (to OpenAI etc.).

  • PII redaction before sending

  • BAA with provider for healthcare

  • EU data residency (Azure OpenAI EU; private GPT)

  • DPA with provider

  • Customer consent

(Path 76 covered compliance.)

Step 9: Continuous Improvement (15 min)

Production app lifecycle:

  • Eval set grows from real failures

  • Prompts refined

  • Models swap (newer / cheaper)

  • New patterns added

  • Retrieval improved

Weekly / monthly cadence.

Tools (LangSmith / Braintrust) make iteration faster.

Step 10: Realistic Expectations (15 min)

  • LLM apps are products, not magic

  • Some queries always wrong

  • Communicate uncertainty to users

  • Plan for "this is wrong" feedback flow

  • Be honest in marketing

Over-promising erodes trust faster than weak features.

Under-promise; over-deliver.

What You Just Did

Prototype to product: the gap, quality bar, eval discipline, failure modes, rollout, human in loop, cost discipline, privacy, continuous improvement, expectations.

You're Done With Path 79

LLM apps: shapes, prompts, structured outputs, RAG, agents, evals, cost/latency, injection defenses, observability, production. LLM application engineering.

Common Failure Modes

Ship demo as product. Quality cliff.

No fallbacks. Provider down = product down.

No cost caps. Surprise bills.

Hype-driven marketing. Disappointed users.

No iteration after launch. Drift.

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