Start Lesson
In hardware engineering, every component ships with a datasheet. A datasheet is not marketing material -- it is a contract between the manufacturer and the engineer. It specifies exact operating parameters: input voltage range, output impedance, thermal limits, mean time between failures, and what happens when you push beyond the rated specs.
When you look at how most teams adopt AI, you realize the industry has no equivalent practice for LLMs. Teams integrate GPT-4 or Claude into production without documenting the operating envelope -- the conditions under which the component performs reliably and what happens when those conditions are violated.
This lesson introduces the practice of creating internal datasheets for every LLM you integrate. It is the single most impactful practice I apply in AI architecture.
Here is the template I use for every LLM integration. Fill this out before writing a single line of integration code:
╔══════════════════════════════════════════════════════════════╗
║ LLM COMPONENT DATASHEET ║
╠══════════════════════════════════════════════════════════════╣
║ Component: Claude 3.5 Sonnet ║
║ Provider: Anthropic ║
║ Use Case: Customer support summarization ║
║ Integration Date: 2026-02-15 ║
╠══════════════════════════════════════════════════════════════╣
║ OPERATING PARAMETERS ║
║ ───────────────────── ║
║ Max Input Tokens: 200,000 ║
║ Max Output Tokens: 8,192 ║
║ Typical Latency: 800ms - 2.5s (p50 - p95) ║
║ Rate Limit: 4,000 RPM / 400K TPM ║
║ Cost Per 1K Input: $0.003 ║
║ Cost Per 1K Output: $0.015 ║
║ Temperature Setting: 0.1 (for this use case) ║
╠══════════════════════════════════════════════════════════════╣
║ FAILURE MODES ║
║ ───────────── ║
║ 1. Rate limit exceeded → 429 response ║
║ 2. API timeout (>30s) → connection reset ║
║ 3. Content filter trigger → blocked response ║
║ 4. Model deprecation → breaking change with notice ║
║ 5. Quality drift → subtle, no error signal ║
╠══════════════════════════════════════════════════════════════╣
║ FALLBACK CHAIN ║
║ ────────────── ║
║ Primary: Claude 3.5 Sonnet ║
║ Secondary: GPT-4o (tested, prompt adapted) ║
║ Tertiary: Cached response template ║
║ Last Resort: Human escalation queue ║
╠══════════════════════════════════════════════════════════════╣
║ MONITORING ║
║ ────────── ║
║ Latency alert: p95 > 5s ║
║ Error rate alert: > 2% in 5-min window ║
║ Cost alert: > $50/day ║
║ Quality check: Weekly sample review (n=50) ║
╚══════════════════════════════════════════════════════════════╝
This is not busywork. Every field in this document has prevented a production incident in my experience. The team that knows their p95 latency is 2.5 seconds designs their UX accordingly. The team that does not discovers it when users start complaining.
In hardware engineering, there is a concept called the "safe operating area" (SOA) -- the combination of voltage, current, and temperature where a component works reliably. Push beyond the SOA, and you get thermal runaway, signal degradation, or outright component failure.
LLMs have an equivalent operating envelope:
Every model has a context window, but the usable window is smaller than the theoretical maximum. I have observed that quality degrades well before you hit the token limit, particularly for tasks requiring reasoning over distributed information. In my systems, I set the practical input limit at 60-70% of the theoretical maximum.
LLM latency is not constant -- it scales with output length and current provider load. A system designed for 500ms responses will behave very differently at 3 AM (low load) versus 2 PM (peak). Always design for p95 or p99 latency, never average.
This is where the hardware analogy becomes financial. A component that costs $0.01 per invocation at 1,000 requests/day costs $10/day. At 100,000 requests/day, it costs $1,000/day. The unit cost did not change, but the system economics shifted fundamentally. I will cover this in depth in Lesson 11.3.
Unlike hardware, where degradation is measurable with instruments, LLM quality degradation is semantic. The model does not throw errors -- it produces subtly worse outputs. This is the hardest dimension to monitor, and it is where most teams get blindsided.
In hardware design, components communicate through standardized interfaces -- SPI, I2C, UART. You can swap a temperature sensor from one manufacturer with another, as long as both comply with the interface specification.
I architect LLM integrations the same way. Every LLM call goes through an abstraction layer that enforces a consistent interface:
// The interface contract -- provider-agnostic
interface LLMComponent {
generate(input: LLMRequest): Promise<LLMResponse>
estimateCost(input: LLMRequest): CostEstimate
healthCheck(): Promise<ComponentHealth>
}
interface LLMRequest {
messages: Message[]
maxTokens: number
temperature: number
metadata: {
useCase: string
costCenter: string
traceId: string
}
}
interface LLMResponse {
content: string
usage: { inputTokens: number; outputTokens: number }
latencyMs: number
model: string
cached: boolean
}
This abstraction is not just good software design -- it is what makes the vendor off-ramp pattern possible. When Anthropic changes their pricing or deprecates a model, my systems switch to the secondary provider with a configuration change, not a code rewrite. This pattern saved one client $60K/month when we identified a more cost-effective model for their highest-volume use case.
Hardware engineers run components through qualification testing before production use: temperature cycling, vibration testing, and accelerated life testing. The equivalent for LLM components is a structured evaluation suite:
Functional testing. Does the model produce correct outputs for a representative sample of inputs? This is your standard eval suite.
Boundary testing. What happens at the edges of the operating envelope? Long inputs, unusual formatting, adversarial prompts, multilingual content.
Stress testing. What happens under load? How does latency degrade? When do rate limits kick in? What is the actual throughput ceiling?
Failure testing. Deliberately inject failures. Kill the network connection mid-stream. Send a prompt that triggers content filters. Simulate a provider outage. Verify that every fallback path actually works.
Drift testing. Run the same eval suite weekly. Track scores over time. Detect quality degradation before users do.
# Simplified drift detection
def run_drift_check(eval_suite, component, baseline_scores):
current_scores = evaluate(eval_suite, component)
for metric, score in current_scores.items():
drift = baseline_scores[metric] - score
if drift > DRIFT_THRESHOLD:
alert(f"Quality drift detected: {metric} "
f"dropped {drift:.2%} from baseline")
trigger_review(metric, component)
store_scores(current_scores, timestamp=now())
Hardware components have a lifecycle: qualification, deployment, monitoring, and end-of-life. LLM components follow the same pattern, but with a compressed timeline. Model deprecations happen with months of notice, not years. New models appear quarterly, not annually.
I maintain a component registry for every production system:
Every component in your system should have a clear status and a documented transition plan.
Not every LLM integration needs a full datasheet and lifecycle process. Here is the decision framework:
| Situation | Approach | Why | |-----------|----------|-----| | Internal tool, < 100 users, low stakes | Lightweight datasheet (cost + fallback only) | The operational overhead of full documentation exceeds the risk | | Production feature, customer-facing | Full datasheet + abstraction layer + drift testing | Customer trust and cost exposure justify the discipline | | Revenue-critical AI feature | Full datasheet + redundant providers + weekly evals | Revenue dependency demands the highest operational maturity | | Prototype or experiment | Skip the datasheet, but note it as tech debt | Move fast, but track that you owe this before production |
The cost of creating a datasheet is roughly two hours per integration. The cost of not having one becomes apparent at 3 AM when your provider degrades and nobody knows what "normal" looks like.
Before promoting any LLM integration to production, verify:
This discipline is what separates AI systems that run for years from AI demos that collapse at scale.
You now have the mental model for treating LLMs as engineered components. The next lesson puts a dollar sign on those components. We will build the unit economics framework that tells you whether your AI feature is profitable -- and the cost engineering strategies that saved one client $60K/month when the answer was "not yet."