The LLM Production Lifecycle

Building AI applications isn't a one-time event. It's a continuous cycle of improvement driven by data and evaluation.

The Build-Evaluate-Deploy-Monitor Loop

    ┌──────────────┐
    │    BUILD     │
    │  Prompts,    │
    │  Agents,     │
    │  RAG         │
    └──────┬───────┘
           │
           ▼
    ┌──────────────┐
    │   EVALUATE   │◄─────────────┐
    │  Test suites,│              │
    │  Benchmarks  │              │
    └──────┬───────┘              │
           │                      │
           ▼                      │
    ┌──────────────┐              │
    │    DEPLOY    │              │
    │  Production  │              │
    │  Release     │              │
    └──────┬───────┘              │
           │                      │
           ▼                      │
    ┌──────────────┐              │
    │   MONITOR    │──────────────┘
    │  Traces,     │
    │  Metrics,    │
    │  Alerts      │
    └──────────────┘

Stage 1: Build

During the build phase, you create or modify:

• Prompts: System instructions, few-shot examples
• Agents: Tool-calling logic, planning strategies
• RAG pipelines: Chunking, retrieval, reranking
• Fine-tuned models: Domain-specific adaptations

Stage 2: Evaluate

Before deploying, you run evaluations:

• Unit tests: Does this prompt produce the expected format?
• Regression tests: Did our changes break existing functionality?
• Quality benchmarks: How does this compare to our baseline?
• A/B comparisons: Is the new version better than the current one?

Key Insight: Evaluation should block deployment if quality drops below your threshold.

Stage 3: Deploy

With passing evaluations, you deploy:

• Gradual rollouts: Start with 5% of traffic
• Feature flags: Toggle between old and new versions
• Canary releases: Monitor the new version closely

Stage 4: Monitor

In production, you continuously:

• Trace every call: Log inputs, outputs, latency, cost
• Track quality metrics: Faithfulness, relevancy, safety
• Alert on anomalies: Quality drops, error spikes, cost overruns
• Collect feedback: User ratings, thumbs up/down

The Feedback Loop

Monitoring data feeds back into the build phase:

1. Discover failing cases in production
2. Add them to your evaluation dataset
3. Fix the issue in your prompts or logic
4. Re-evaluate to confirm the fix
5. Deploy with confidence

Next, let's explore the key metrics that define LLM quality. :::