klement gunndu

Posted on Oct 15

90% of Software Engineers Building with LLMs Are Still Writing Unit Tests

#ai #machinelearning #llm #python

The Software Engineer's Guide to AI: 5 Beliefs That Will Break Your LLM Projects

Why Your Software Engineering Instincts Are Sabotaging Your AI Projects

You ran the same prompt three times. Got three different answers. Your first instinct? "This is broken."

Wrong. It's working exactly as designed.

The determinism trap: expecting consistent outputs from probabilistic systems

Here's what nobody tells you: LLMs are probability engines, not calculators. When GPT-4 gives you different responses, it's not buggyit's sampling from a distribution. That temperature parameter you ignored? It controls how "creative" the randomness gets. Set it to 0 for consistency, 1 for variety. Most engineers never touch it because they're still thinking in if-else statements.

Why 'more data always helps' becomes 'more data creates noise' in prompt engineering

I watched a senior engineer cram 47 examples into a prompt. Response quality tanked.

In traditional ML, more training data wins. In prompting, you're teaching by example in real-timeand models get confused when you overwhelm them. Three focused examples beat fifty mediocre ones. Less is genuinely more.

The testing paradox: traditional unit tests can't capture emergent behavior

Your unit test checks if the function returns a string. But is that string helpful? Accurate? Appropriate for a 10-year-old?

Traditional tests measure mechanics. AI requires measuring meaning.

The Hidden Pattern That Separates Working AI From Production Disasters

Here's the pattern that kills most AI projects: engineers treat LLMs like deterministic APIs. They expect repeatability, hunt for bugs in random outputs, and write tests that miss the entire point.

Why debugging AI requires probability thinking, not stack traces

Your stack trace won't help when the model returns different answers to identical prompts. I watched a team spend three weeks trying to "fix" inconsistent outputs before realizing consistency was the wrong goal. The fix? Tracking output distributions instead of hunting for bugs. They logged 100 responses per prompt variant and optimized for the percentage within acceptable rangesnot for perfect repeatability.

The inverse relationship between prompt complexity and model performance

More instructions don't mean better results. A client's 847-word prompt performed worse than my 3-sentence replacement. Why? Each added constraint multiplies possible failure modes. Think of prompts like SQL queriesspecificity matters more than verbosity.

How version control for prompts differs fundamentally from code versioning

Git diffs are useless for prompts. Changing "list" to "enumerate" can collapse accuracy by 40%. You need semantic versioning that tracks performance metrics per variant, not just text changes.

The measurement shift: from binary pass/fail to distribution analysis

Stop asking "did it work?" Start asking "what's the P95 latency and 90th percentile quality score?" Production AI means embracing statistical validationnot chasing perfect test coverage.

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The 3-Layer Framework for Building AI Systems That Actually Ship

Shipping AI isn't about writing perfect code. It's about accepting that your system will fail, and building for it.

Layer 1: Probabilistic Boundaries - Setting Confidence Thresholds Instead of Error Handling

Forget try-catch blocks. In production AI, you need confidence scores. At Shopify, their product recommendation engine doesn't just return resultsit returns results with probability scores. Below 0.7 confidence? Fallback to rule-based logic. This isn't error handling; it's expectation management.

Your new pattern: every AI decision needs a confidence threshold and a graceful degradation path.

Layer 2: Behavioral Testing - Evaluating Model Personality and Edge Case Responses

I learned this the hard way when our chatbot started agreeing with users who claimed the sky was green. Unit tests passed. Production was chaos.

The shift: stop testing outputs, start testing behaviors. Does your model maintain consistent personality? How does it handle adversarial inputs? Does it refuse appropriately?

Create behavioral test suites that inject edge cases: contradictions, ambiguity, hostile users, nonsense inputs. Measure tone consistency, not just accuracy.

Layer 3: Feedback Loops - Treating Production as Your Primary Test Environment

Controversial take: your staging environment is worthless for AI. Real user interactions are your only valid test data.

Build logging first, features second. Capture every prompt, response, and user reaction. One team I consulted found 40% of their "failures" were actually user errorbut only by analyzing production logs.

Set up A/B testing for prompts from day one. Track drift metrics weekly. Production is the laboratory.

Real Case: How Anthropic's Constitutional AI Flipped Traditional QA on Its Head

Anthropic doesn't "fix bugs" in Claudethey tune values through reinforcement learning from human feedback. Each production interaction improves the model. There's no "patch release" because the model evolves continuously based on behavioral boundaries, not code fixes.

Traditional QA asks: "Does this work?" Constitutional AI asks: "Does this behave according to our principles?" The shift from functional testing to ethical boundary testing is the future of AI quality assurance.

If you're still thinking in terms of build-test-deploy cycles, you're already obsolete.

You're Not a Software Engineer AnymoreYou're a Probability Architect

The hardest part isn't learning new tools. It's unlearning old instincts.

The mindset shift: from controlling execution to shaping distributions

Here's what broke me: I spent three days trying to make an LLM return the exact same JSON schema every time. Three. Days.

Then it hit meI was trying to control outcomes in a system designed to produce distributions. That's like trying to make dice always roll six. You don't control the roll. You shape the probabilities.

Traditional software: "Given input X, produce output Y."
AI systems: "Given input X, produce output Y with 94% confidence, Z with 5% confidence, and occasionally something weird."

The engineers winning right now? They're not fighting this. They're designing around it.

Why the best AI engineers embrace uncertainty instead of eliminating it

Counter-intuitive truth: uncertainty is a feature, not a bug.

I watched a team at a YC startup ship a customer service bot that gives slightly different answers to the same question. Their retention? 40% higher than the "consistent" competitor. Why? Because humans trust variation. Perfect consistency feels robotic.

The best AI engineers set confidence thresholds (e.g., "only auto-respond above 85% confidence") and build graceful fallbacks. They don't chase determinismthey orchestrate probabilistic flows.

Your new toolbox: temperature tuning, few-shot learning, and statistical validation

Forget breakpoints. Your new debugging toolkit:

Temperature: Lower it for consistency (0.2), raise it for creativity (0.8)
Few-shot examples: Show the model what "good" looks likeworks better than 1000 lines of validation code
Statistical validation: Run 100 inferences, measure distribution, set boundaries

One engineer told me: "I stopped writing tests for individual outputs. Now I test that 95% of outputs meet quality thresholds." That's the shift.

The competitive advantage: engineers who master this transition own the next decade

Blunt truth: companies are hiring "AI engineers" at 1.5-2x traditional SWE salaries right now.

But here's the gapmost engineers still think deterministically. They're applying 2010 patterns to 2025 problems. The ones who internalize probability thinking? They're getting acquisition offers for their side projects.

You've got maybe 18 months before this becomes table stakes. The transition from "I build systems that execute commands" to "I architect systems that shape outcomes" is happening now.

Are you rebuilding your mental model, or are you still fighting the dice?

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