Why LLM Pipelines Break in Production
Most LLM demos work perfectly in a notebook. The moment you add real users, rate limits, context windows, and latency requirements — everything falls apart. Here's what I've learned building production LLM systems.
Designing for Observability First
Before writing a single prompt, instrument everything. Every LLM call should emit: input tokens, output tokens, latency, model version, and a trace ID linking it to the user request.
What to Log
Log the full prompt + completion at debug level, truncated at info level. Never log PII. Use structured JSON logs so you can query them in Grafana or Datadog.
Trace IDs Are Non-Negotiable
A trace ID that flows from the HTTP request through every LLM call, database query, and external API call is the single most important thing you can add to any AI system. Without it, debugging is archaeology.
Handling Rate Limits Gracefully
Every LLM provider has rate limits. Build exponential backoff with jitter from day one. Use a token bucket or leaky bucket algorithm at the application layer before hitting the API.
Async Queue Pattern
For batch workloads, never call the LLM synchronously from a web request. Push jobs to a queue (Redis, SQS, BullMQ) and process them with workers. This decouples your API latency from LLM latency completely.
Context Window Management
The hardest problem in LLM engineering isn't prompting — it's deciding what goes into the context window. Every token costs money and adds latency.
Chunking Strategies
Fixed-size chunking is simple but lossy. Semantic chunking (splitting on paragraph or sentence boundaries) preserves meaning. Recursive character splitting with overlap is the sweet spot for most RAG use cases.
Retrieval Augmented Generation
RAG is not just vector search. It's a pipeline: chunk → embed → store → retrieve → rerank → inject → generate. Each step has failure modes. Reranking is the most underrated step — a cross-encoder reranker can dramatically improve retrieval quality.
Cost Optimization Without Sacrificing Quality
Running GPT-4 on every request will bankrupt you. Build a model routing layer that sends simple classification tasks to smaller models and complex reasoning to frontier models.
Prompt Caching
Anthropic's prompt caching can cut costs by 90% on repeated system prompts. Cache your large system prompts and only pay for the variable portion of each request.
Lessons Learned
The best LLM pipeline is the one you can debug at 3am. Prioritize observability, build for failure, and never trust a demo that hasn't been load tested.
