Integrating AI Into Production Workflows (Beyond the Demo)
01Moving Beyond the Demo
Demos love stateless prompts. Production needs observability, guardrails, and fallbacks. We treat AI features like any other service: defined inputs, typed outputs, rate limits, and explicit failure modes.
When an LLM call fails, the user should not see a raw API error. Graceful degradation is crucial. If the assistant is down, can the user still achieve their goal manually?
“Users forgive a wrong suggestion. They do not forgive losing their draft because the model timed out.”
02Retrieval-Augmented Generation
Retrieval quality beats model size for most product questions. We invest in chunking, metadata, and evaluation sets before tuning temperature. If the knowledge base is wrong, no model rescues the experience.
- 01Chunk by semantic section, not arbitrary token windows
- 02Store source URL, updated_at, and permission scope in metadata
- 03Log retrieved chunks with each answer for debugging
- 04Measure faithfulness, not just fluency
We often see teams swapping models hoping for better answers, when the real issue is that vector search is returning irrelevant context. Fix retrieval first.
03Human-in-the-Loop
Human-in-the-loop remains essential for high-stakes actions — payments, account changes, legal content. Assistants suggest; systems confirm.
“Autonomy is a dial, not a switch. Turn it up only where reversibility is cheap.”
04Incremental Rollout
Ship incrementally: read-only Q&A first, then guided flows, then automation with audit logs. Users trust the product when behavior is boringly reliable.
Monitor hallucination rates and adjust system prompts based on real-world usage. AI features are never truly done — they require continuous tuning.