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Building Effective AI Pipelines for Production

· 2 min read
Jordan Park
Jordan Park
ML Engineer

Moving an AI prototype from a notebook to a reliable production system is where most teams struggle. Here's how we approach building AI pipelines that actually work at scale.

The Pipeline Mindset​

A production AI system isn't a single model — it's a pipeline of stages, each with its own reliability requirements:

Ingestion → Preprocessing → Embedding → Retrieval → Generation → Post-processing → Output

Each stage needs monitoring, error handling, and fallback strategies. Treating any single component as the "AI part" is a common mistake.

Key Architecture Decisions​

Synchronous vs. Asynchronous​

Not every AI task needs a real-time response. We categorize workloads into two tiers:

PatternLatencyUse Case
Synchronous< 2 secondsChat interfaces, inline suggestions
AsynchronousMinutes to hoursDocument processing, batch analysis, report generation

Async pipelines are simpler to build, easier to scale, and more cost-effective. Default to async unless the user experience demands real-time.

Chunking Strategies​

How you split documents for processing has a massive impact on quality:

  • Fixed-size chunks — Simple but can break mid-sentence
  • Semantic chunking — Splits at paragraph or section boundaries
  • Recursive chunking — Progressively splits until chunks meet size constraints
  • Agentic chunking — Uses an LLM to determine natural breakpoints

We've found that semantic chunking with overlap windows gives the best balance of retrieval quality and simplicity.

Embedding and Retrieval​

Vector search is powerful but not always sufficient. We often combine multiple retrieval strategies:

Query → [Vector Search]  → Top K results  ─┐
      → [Keyword Search] → Top K results  ─┼→ Re-rank → Final results
      → [Metadata Filter] → Filtered set  ─┘

This hybrid approach catches cases where semantic similarity alone misses relevant results.

Monitoring in Production​

AI pipelines fail in subtle ways that traditional monitoring doesn't catch:

  • Latency drift — Model response times gradually increasing
  • Quality degradation — Output quality declining as input distribution shifts
  • Cost spikes — Token usage growing unexpectedly
  • Hallucination rates — Factual accuracy dropping over time

We track all four and set alerts at thresholds that trigger human review before users are affected.

Start Simple, Iterate Fast​

The best AI pipelines we've built started as the simplest possible version that delivered value. Complexity should be earned through measured improvements, not assumed upfront.