Why this matters in interviews

System design rounds have changed.

Alongside the usual questions on databases, caching, and queues, interviewers now ask you to design applications built on LLMs. And once an LLM is in the design, the first question is which model you'd use and why. This course covers how to make that choice and how to explain it, with the same rigor you'd apply to picking Postgres over DynamoDB, applied to picking a model.

Ask a candidate why they chose a particular database and you'll usually get a real answer: read patterns, consistency requirements, operational cost, team familiarity. Ask the same candidate why they chose a particular model and the answer is often "GPT is the best one" or "we'd use whatever is on top of the leaderboard."

Interviewers notice the difference. Model selection has the same structure as any other architecture decision. There are constraints, there are trade-offs, and there is a wrong answer for a given set of requirements. If you can talk about capability tiers, cost at scale, latency budgets, and data residency the way you'd talk about sharding or replication, you're demonstrating that you understand the system rather than the API.

Several lessons in this course deal with interview framing directly, including how to answer the safe model upgrade question, which has become a common follow-up.

Also useful if you're building this for real

The framework isn't only interview theory.

It's the same reasoning you need when you're the person who has to justify a model choice on the job.

If you're shipping LLM features today, the parts of this course on evals, model drift, and routing are directly applicable. Teams routinely overpay by an order of magnitude by sending simple tasks to frontier models, and routinely get caught out when a provider updates a model underneath them. Both are solvable problems with known patterns.

What the course covers

Five things you'll be able to do by the end.

  • A four-axis framework

    Capability, cost, latency, and privacy. Four questions that between them narrow "which model" down to a short list you can actually reason about.

  • How to read a model announcement

    Benchmark scores are marketing when providers publish them. Learn what each benchmark category measures, which ones still separate frontier models, and how contamination, cherry-picking, and hidden evaluation settings inflate published numbers.

  • How to run evals

    Curated example sets instead of ad hoc testing, three ways to score outputs, how to correct for the biases in LLM-as-judge setups, and how to build a golden dataset you'll actually maintain.

  • How to handle model drift

    Regression thresholds, snapshot pinning, and an eval gate so a provider update doesn't reach your users before you've seen it.

  • The model router pattern

    When a small model does the job at a fraction of the cost, how the common routing strategies compare, what safeguards a router needs, and when routing isn't worth the complexity.

Course curriculum

    1. Why Model Choice Is a System Design Problem

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    2. Specialization Trade-offs and Why Gaps Persist

    3. Four Forces Driving the Model Explosion

    4. The Model Explosion From 2024 to 2026

    1. The Four-Axis Model Selection Framework

    2. Capability as a Profile, Not a Score

    3. Capability Tiers From Frontier to Specialized

    4. Cost at Scale and Why Frontier Prices Keep Rising

    5. How Open Weights Changed Who Can Build Models

    6. Geopolitics and Compliance Constraints

    7. Latency TTFT and Total Generation Time

    8. Privacy as the Strictest Filter

    9. The Privacy Deployment Spectrum

    10. Applying the Framework to Real Scenarios

    1. Provider Philosophy and OpenAI's Ecosystem Bet

    2. Anthropic's Constitutional AI

    3. Google's Hardware and Data Advantage

    4. Meta's Open Weight Strategy and the Platform War

    5. Why Standard LLMs Cannot Truly Reason

    6. Giving Models Hidden Reasoning

    7. How Inference-Time Compute Scales Capability

    8. Practical Costs and Limits of Reasoning Tokens

    9. How Models Are Trained to Reason

    10. Tuning the Reasoning Effort Dial in Production

    1. Why Model Selection Needs More Than a Leaderboard

    2. Benchmark Categories and What Each One Measures

    3. Reasoning, Math, Human Preference, and Long-Context Benchmarks

    4. The Saturation Treadmill and Benchmarks That Still Discriminate

    5. Hidden Variables That Change Benchmark Scores

    6. How to Read a Model Announcement Critically

    7. Audience Questions

    8. The Five Assumptions Behind Every Benchmark

    9. Contamination and Artificially Inflated Scores

    10. Goodhart's Law and How Providers Game Benchmarks

    11. Saturation and Cherry-Picking in Published Results

    1. What Production Requires That Benchmarks Never Measure

    2. Defining Model Quality on Your Own Terms

    3. Audience Questions 2

    4. What an Eval Is and Why Assertions Fail for LLMs

    5. Replacing Vibe Checks with a Curated Example Set

    6. Three Ways to Score Eval Outputs

    7. Judge Biases and How to Correct Them

    8. Building a Golden Dataset and Choosing Eval Tooling

    9. Audience Questions 3

    10. Handling Model Drift with an Eval Gate

    11. Regression Thresholds and Snapshot Pinning

    12. Answering the Safe Model Upgrade Interview Question

    13. Audience Questions 4

    1. When Small Models Beat Frontier Models on Cost

    2. The Model Router Pattern End-to-End

    3. Routing Strategies and Their Trade-offs

    4. Router Safeguards and When Not to Route

    5. Audience Questions 5

About this course

  • $49.99
  • 53 lessons
  • 5.5 hours of video content

Who this is for

If you've called an API, you have enough to follow along.

  • Engineers preparing for senior or staff-level system design interviews where LLM applications come up.

  • Engineers and architects already building on model APIs who need to make and defend these decisions at work.

  • No Python and no machine learning background required. There's no training or fine-tuning here.

What makes this different

Built to outlast the current model lineup.

  • A framework, not a model comparison

    Specific models go stale within months. The four axes, the benchmark skepticism, and the eval discipline apply to whatever ships next.

  • Two full units on benchmark skepticism

    Why published benchmark scores mislead and what to measure instead. Most model comparisons skip that entirely.

  • It ends in production patterns

    Eval gates, snapshot pinning, regression thresholds, and routing with safeguards: the things that come up in interview follow-up questions and in real incident reviews.

  • Five audience Q&A sessions

    Spread through the course, covering the objections and edge cases that come up when people try to apply this.

FAQ

The questions people ask before they enroll.

  • Do I need to know Python or machine learning?

    No. There's no training, fine-tuning, or notebook work in this course. If you've called an API, you have the background.

  • Will this be outdated when the next model ships?

    The framework is built to outlast specific models. Individual models appear as examples of a category, not as recommendations to memorize.

  • Is this only for interview prep?

    No. Interview prep is the fastest reason to take it, but Units 4 through 6 are production material: evaluating whether a model works for your use case, catching drift, and cutting cost with routing.

  • My company has standardized on one provider. Is this still useful?

    Yes. Most of the eval, drift, and routing material applies within a single provider's model family.

Make the decision you can explain.

53 lessons on choosing, validating, and operating the models your system depends on.

Enroll now