Taking LLM Apps from Demo to Production: 10 Pitfalls

Over the past two years I’ve watched plenty of LLM projects shine in demos and fall apart in production. The model isn’t the problem — the team is, by carrying demo habits straight into production. Here are the ten pitfalls I see most often.

1. Treating a demo as the product

Demos run on a handful of hand-picked samples with hardcoded prompts. Production inputs are endless and varied. Fix: build a long-tail input set from day one and hold the worst cases as your acceptance bar.

2. Treating hallucination as a bug

Hallucination isn’t a bug; it’s a systemic property of LLMs. Launching without fact-checking means shipping wrong answers to users. Fix: ground key facts with RAG and cite sources; add rules or human fallback for high-impact outputs.

3. No latency budget

A single LLM call takes 300ms to seconds; multi-step agents can pile up to minutes. Classic P99 instincts stop applying. Fix: redesign the interaction — async, streaming, pre-generation — hide waiting inside the product.

4. No cost model

Token costs scale linearly with traffic; one unbounded loop can burn a month’s budget. Fix: model cost by QPS, context length and retry rate before launch; add circuit breakers and budget alerts.

5. No evaluation set

Traditional software has unit tests; LLM apps have no assertions. Without an eval set there is no regression. Fix: build an eval set covering typical and edge cases; run it on every prompt change or model swap so “feels better” becomes a number.

6. Missing cache and degradation

Third-party model APIs throttle, jitter and go down. Without caching and fallbacks, one upstream outage is an incident. Fix: semantic result caching, graceful degradation to local models or canned replies, keep the core path alive.

7. Ignoring prompt injection

Concatenating external input into your prompt hands the system prompt to users. Fix: filter input and output, authorize tool calls and sensitive operations, treat LLM output as untrusted data.

8. Sloppy context management

Context gets slower and pricier as it grows; unbounded concatenation blows the window. Fix: truncate, compress with summaries, retrieve only relevant fragments — manage context as a resource.

9. No observability

Classic systems watch error rate, latency, CPU; LLM systems need token spend, context length, retry rate and, crucially, answer-quality signals. Fix: instrument the full path and feed user feedback back so quality issues are traceable.

10. Underestimating the organizational problem

AI projects get scheduled like CRUD work with vague acceptance criteria. Fix: make “eval set passing” the acceptance gate, define where AI ends and traditional logic begins, and align the team on uncertainty.

Wrap-up

Productionizing LLM apps is, at heart, a catch-up in engineering discipline. The stronger the model, the more you must argue with latency, cost and observability. Avoid these ten and your project has only just begun.