Ai Coding And Product Systems easy
Your code review bot must enforce 300 team-specific style and security rules. Pasting all 300 into every prompt is slow and expensive, and the model ignores many of them. What is the better design?
- A Fine-tune the model on all 300 rules once, then never include any rule text in review prompts.
- B Retrieve only the rules relevant to each diff chunk, by language and path.
- C Keep all 300 rules in every prompt but bold the key ones so the model weighs them more heavily.
- D Drop the custom rules and rely on the model's general knowledge of best practices per language.
Answer: B. Retrieve only the rules relevant to each diff chunk, by language and path.
**Rule retrieval** keeps prompts short and puts the applicable rules where the model will attend to them, at the cost of maintaining rule metadata. Fine-tuning (A) sounds efficient, but rules change often and fine-tuned behavior is hard to audit or update quickly. Including every rule in each prompt is fine only when the rule set is small, a dozen or so.
One system design question a day, with spaced repetition so the ones you miss come back.
Get the appRelated questions
- Your assistant uses a classifier to route queries to a small or large model, which saves 60% of GPU spend. Analysis shows about 20% of hard queries are misrouted to the small model, which then gives confident wrong answers, and regenerate clicks on those threads have doubled. What should you do?
- Your workspace AI retrieves the top-k chunks from a shared vector index and passes them to the model with ACL metadata and a system prompt saying 'only use documents this user can access.' A user just received an answer quoting a private HR page they cannot open. What is the correct fix?
- Your support bot answers billing questions. One user has asked the same refund question three times, and retrieval confidence for that topic is low. What should the bot do next?
- Your AI review system receives a 4,000-line pull request spanning 60 files. Sending the whole diff in one prompt yields shallow, generic comments, and real bugs in files near the middle of the diff are missed. How should you structure the review?
- In your ChatGPT-style assistant, power users keep single threads running for weeks, reaching about 150K tokens. Cost per turn keeps climbing, latency is rising, and the model increasingly misses facts from early in the thread. What should you do?
- Your workspace AI filters retrieval using ACLs stored in the vector index, which is rebuilt nightly. An admin removes an employee from a confidential project page, but for the rest of the day the employee can still get AI answers drawn from it. What is the right fix?