Stop Treating GenAI Like a Feature — It’s a System

This guide reframes GenAI as a system, not a UI add‑on. It starts with system‑specific concepts, then moves into production architecture and implementation steps with validation.

Core GenAI Concepts

Input contract: strict schema and limits for requests. Behavior: invalid inputs are rejected at the edge. Pitfall: accepting free‑form inputs causes non‑deterministic outputs.
Context assembly: controlled process that builds the prompt from retrieved data. Behavior: bounded, ranked, and traceable. Pitfall: unbounded context increases latency and cost.
Output contract: schema‑validated response format. Behavior: invalid outputs are retried or rejected. Pitfall: unvalidated outputs break downstream automation.
Execution budget: explicit caps on tokens, latency, and retries. Behavior: enforces predictable cost. Pitfall: no budget leads to unbounded spend.

Architecture

A production GenAI system has five components:

Input gateway: validates schema, size, and safety rules.
Context builder: retrieval, ranking, truncation, and context provenance.
Model runtime: controlled inference parameters and timeouts.
Output validator: schema validation with retry policy.
Telemetry + controls: logging, budgets, and incident response.

This design fits GenAI because model outputs are probabilistic; stability comes from contracts, validation, and operational controls around the model.

System Boundary Example (Real World)

Consider a support summarization service. The UI surface is small, but the system spans input validation, retrieval, model inference, and output normalization. If you treat it like a feature, you will ship without contracts, and failures will appear as random. A system view forces explicit policies: which requests are accepted, how context is selected, what happens on model failure, and which outputs are allowed to flow into downstream automation.

In production, these boundaries show up in logs and incident response. When a summary is wrong, the question is not “Why did the model do that?” It is “Which system boundary failed to enforce the contract?” If input validation passed a malformed request, or the context builder pulled irrelevant documents, the system is at fault. Your model is only one component in a chain.

Step-by-Step Implementation

Step 1: Define Input and Output Contracts

Purpose: enforce invariants and prevent drift.

import jsonschema

INPUT_SCHEMA = {
  "type": "object",
  "required": ["request_id", "task", "user_context"],
  "properties": {
    "request_id": {"type": "string"},
    "task": {"type": "string", "maxLength": 4000},
    "user_context": {"type": "string", "maxLength": 8000}
  }
}

OUTPUT_SCHEMA = {
  "type": "object",
  "required": ["summary", "next_steps"],
  "properties": {
    "summary": {"type": "string"},
    "next_steps": {"type": "array", "items": {"type": "string"}}
  }
}

def validate_input(payload: dict) -> None:
    jsonschema.validate(payload, INPUT_SCHEMA)

Validation: requests failing schema are rejected with a 4xx error.

Step 2: Build a Bounded Context Assembly

Purpose: keep cost and latency predictable while preserving relevance.

import os

MAX_CONTEXT_CHARS = int(os.environ.get("MAX_CONTEXT_CHARS", "12000"))
MAX_CHUNKS = int(os.environ.get("MAX_CHUNKS", "6"))

def assemble_context(ranked_chunks):
    selected = ranked_chunks[:MAX_CHUNKS]
    context = "\n".join(chunk.text for chunk in selected)
    return context[:MAX_CONTEXT_CHARS]

Validation: context length never exceeds configured limits; selected chunk IDs are logged.

Step 3: Enforce Model Runtime Budgets

Purpose: keep inference deterministic in cost and latency.

import os

MAX_OUTPUT_TOKENS = int(os.environ.get("MAX_OUTPUT_TOKENS", "400"))
MAX_RETRIES = int(os.environ.get("MAX_RETRIES", "3"))
TIMEOUT_SECONDS = int(os.environ.get("MODEL_TIMEOUT_SECONDS", "20"))

Validation: requests exceeding limits are rejected; runtime enforces timeout.

Step 4: Validate Outputs with a Retry Policy

Purpose: prevent malformed outputs from reaching downstream systems.

import json
import jsonschema

def validate_or_retry(call_model):
    for attempt in range(MAX_RETRIES):
        raw = call_model(timeout_seconds=TIMEOUT_SECONDS, max_tokens=MAX_OUTPUT_TOKENS)
        try:
            data = json.loads(raw)
            jsonschema.validate(data, OUTPUT_SCHEMA)
            return data
        except Exception:
            if attempt == MAX_RETRIES - 1:
                raise

Validation: only schema‑valid outputs are returned; retries are capped.

Step 5: Add Observability and Cost Controls

Purpose: make failures and spend visible in production.

import logging

logger = logging.getLogger("genai")
logger.setLevel(logging.INFO)

BUDGET_DAILY_USD = 50
ALERT_THRESHOLD_USD = 45

def record_metrics(request_id, latency_ms, token_count, spend_today):
    logger.info(
        "genai_request",
        extra={
            "request_id": request_id,
            "latency_ms": latency_ms,
            "token_count": token_count,
            "spend_today": spend_today,
        },
    )
    if spend_today >= ALERT_THRESHOLD_USD:
        send_budget_alert()
    if spend_today >= BUDGET_DAILY_USD:
        raise RuntimeError("budget_exceeded")

Validation: budget alerts trigger before caps; request logs include latency and token count.

Step 6: Define a Fallback Path

Purpose: return a safe response when the model fails or violates contracts.

def fallback_response(request_id):
    return {
        "summary": "We could not complete this request automatically.",
        "next_steps": ["Escalate to human review", f"Reference ID: {request_id}"],
    }

Validation: fallback responses always conform to the output contract.

Step 7: Implement the Model Runtime Client

Purpose: centralize model invocation with timeouts, retries, and logging.

import os
import time
import logging
from openai import AzureOpenAI

logger = logging.getLogger("genai_runtime")
logger.setLevel(logging.INFO)

client = AzureOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    api_key=os.environ["AZURE_OPENAI_API_KEY"],
    api_version="2024-02-01"
)

MODEL = os.environ.get("MODEL_DEPLOYMENT", "gpt-4.1-mini")

def call_model(prompt: str, timeout_seconds: int, max_tokens: int) -> str:
    start = time.time()
    resp = client.responses.create(
        model=MODEL,
        input=prompt,
        max_output_tokens=max_tokens,
        timeout=timeout_seconds
    )
    latency_ms = int((time.time() - start) * 1000)
    logger.info("model_call_ok", extra={"latency_ms": latency_ms})
    return resp.output_text or ""

Validation: timeouts are enforced and latency is logged for every request.

Step 8: Define SLOs and Error Budgets

Purpose: operationalize reliability expectations.

Latency SLO: p95 <= 1.2s for 95% of requests.
Error rate: <= 1% of requests fail validation or runtime.
Budget: daily spend caps per tenant.

Validation: alerts fire when SLOs or budgets are breached.

Step 9: Security and Data Handling

Purpose: prevent sensitive data leakage.

Redact PII before logging.
Encrypt stored prompts and outputs.
Separate production and staging keys.

Validation: security checks run in CI and access audits are logged.

Operational Guidelines

Request traceability: every request must carry a request_id that propagates across context retrieval, model call, and output validation.
Context provenance: store which documents or chunks were used. This is required for debugging and for compliance audits.
Prompt versioning: treat prompts as artifacts. A prompt change is a code change and must be reviewed.
Rate limiting: protect upstream services and cost budgets. Implement per‑tenant rate limits and global caps.

Real-World Failure Modes

Context drift: retrieved content no longer matches the current task. Fix by monitoring retrieval relevance and periodically refreshing indexes.
Schema drift: output formats evolve silently. Fix with strict validation and backward‑compatible contracts.
Budget spikes: long contexts or repeated retries inflate spend. Fix with caps and alerting.

Incident Response Expectations

Triage by request_id and reconstruct the context assembly step.
Compare output against the schema and identify validation failures.
Roll back prompt or model changes that correlate with the incident window.

When This Approach Is Too Heavy

If you are running a short‑lived prototype or exploring new ideas, the full system approach can slow you down. Use a lightweight pipeline, but keep at least minimal contracts and request logging so you can evolve safely.

Common Mistakes & Anti-Patterns

No contracts: downstream failures become random. Fix: enforce input/output schemas.
Unlimited context: cost spikes. Fix: cap and rank context.
No validation loop: malformed outputs leak. Fix: validate + retry.
Budgetless inference: runaway spend. Fix: enforce max tokens and daily caps.

Testing & Debugging

Use a golden set for regression testing.
Log request IDs, context size, validation outcomes, and token usage.
Replay failed requests to reproduce issues and inspect context assembly.
Record and diff prompt versions to isolate regressions.

Trade-offs & Alternatives

Limitations: added complexity and latency.
When not to use: small prototypes or one‑off tasks.
Alternatives: deterministic rule‑based systems or traditional ML pipelines.

Final Checklist

Input/output schemas enforced
Context limits configured
Output validation with retries
Telemetry and cost controls enabled
Fallback path defined