AI-Assisted Prototyping Workflow
"Vibe coding" patterns for builders: translating an idea into a deployed prototype using AI tools, iterating in real time, knowing when to accept scaffolded code vs. when to refactor. Covers tool selection, prompt-to-code pipelines, and demonstrating builder velocity in interviews.
Context Window Management & RAG Basics
Practical knowledge of context windows, token limits, chunking strategies, and retrieval-augmented generation (RAG) for apps that need to work with large docs or knowledge bases. Expected background knowledge for AI product engineers at Google, Meta, and Microsoft.
LLM Evaluation & Quality Gates
How to assess LLM output quality in production: golden test sets, human evals, model-based grading (LLM-as-judge), latency/cost trade-offs, and regression prevention. Critical for any role building AI-native products at scale — emphasized in Microsoft Copilot and Amazon GenAI platform roles.
Prompting & AI Agent Orchestration
How to effectively direct AI coding agents (Claude Code, Cursor, GitHub Copilot) to build features rapidly. Covers prompt design, task decomposition, output evaluation, and knowing when to accept vs. rewrite AI-generated code. Key for Microsoft Copilot team "Member of Technical Staff" and LinkedIn Full Stack Builder roles.
Articulating Your Builder Vision
How to communicate the "why" behind what you're building in interviews. Product storytelling for builders: connecting user pain → insight → solution → impact. LinkedIn's Tomer Cohen identifies Vision as the #1 non-automatable skill — interviewers at LinkedIn, Microsoft, and Meta probe this directly.
Builder Judgment: Ship vs. Refactor
Demonstrating the technical and product judgment that separates senior builders from juniors: when to accumulate technical debt for speed, when to refactor, and how to communicate trade-offs to stakeholders. Cohen's research found top performers benefit most from AI — because judgment amplifies leverage.
Collaborating Without Handoffs
How Full Stack Builders work with PMs, designers, data scientists, and platform engineers. Async communication patterns, lightweight documentation, reducing review bottlenecks, and demonstrating cross-functional ownership. Key for LinkedIn, Meta, and Amazon builder roles where multi-functional output is expected.
From PRD to Working Prototype
Translating a product requirements document into a working software artifact with AI assistance. Covers scope tightening, tech stack selection, iterative shipping, and how to compress the traditional design → build → review cycle from weeks to days.
Full-Cycle Feature Ownership
The builder mindset: owning a feature from user research → design → build → instrument → iterate, without handing off to separate functions. Core to LinkedIn's Full Stack Builder program and Meta's expectation that AI builders replace the "conveyor belt" between PM, design, and eng.
Rapid Experimentation & Instrumentation
How to run product experiments as a builder: feature flags, A/B testing setup, embedding basic analytics (event tracking, funnels), and using data to iterate fast. Expected in Amazon's "builder" SDE roles and LinkedIn's Full Stack Builder program.
Scope & Simplicity: Shipping the 80% Solution
Builder judgment on cutting scope without cutting quality. How to identify the smallest shippable unit, avoid gold-plating, negotiate with stakeholders, and articulate why simplicity is a feature — a key skill LinkedIn, Microsoft, and AI startups test for in builder interviews.
AI-Native Application Architecture
Designing apps with LLMs at the core: RAG pipelines, vector stores, agent frameworks (LangChain, LlamaIndex), tool use, streaming responses, and latency management. The system design equivalent for Full Stack Builders — emphasized at Microsoft (Copilot), Google (Gemini products), and AI-native startups.
API Design for Builder-Led Products
REST vs GraphQL, API-first development, versioning strategies, and how to design APIs that survive rapid builder iteration. Covers webhook patterns, streaming APIs (SSE vs WebSocket), and how Microsoft's "Member of Technical Staff" roles expect full-stack ownership of API contracts.
Deployment & Observability for Builders
Getting a builder-created app to production: containerization basics (Docker), serverless deployments (Vercel, AWS Lambda), environment management, logging, and basic alerting. Amazon and Microsoft expect SDEs and MTS candidates to own full deployment pipelines.
Shipping Polished Interfaces Fast
Leveraging component libraries (shadcn/ui, Radix, MUI) and AI-generated UI (v0, Lovable, Bolt) to ship quality interfaces without a dedicated designer. Knowing when "good enough" is right vs. when craft matters — and how to discuss UI decisions in interviews.
UX Thinking for Builders
Applying design thinking without being a designer: user journeys, Jobs-to-be-Done, wireframing with AI (v0, Figma AI), and the design systems era (shadcn/ui, Tailwind). LinkedIn's Full Stack Builder vision and Meta's AI builder program both require builders to internalize user empathy as a non-automatable skill.
Handling a Failed Product / Decision
How to discuss failures with accountability, learnings, and growth. Avoid blame-shifting and show self-awareness.
Tell Me About a Product You Launched
STAR-format answer covering: the problem, your role, cross-functional coordination, launch decision, outcome, and learnings.
Build vs Buy vs Partner Decision
Framework for make/buy/partner decisions: strategic fit, speed, cost, capability gaps, and vendor risk.
Managing Conflicting Stakeholder Priorities
How to handle conflicting demands from engineering, design, sales, and leadership. Escalation paths, alignment techniques.
Prioritization Frameworks (RICE, ICE, MoSCoW)
Compare and apply RICE (Reach × Impact × Confidence / Effort), ICE, and MoSCoW frameworks for backlog prioritization.
A/B Testing Fundamentals
Statistical significance, p-values, sample size calculation, novelty effect, experiment design pitfalls, and how to interpret results.
Define Success Metrics for a Feature
How to pick and prioritize metrics: goal alignment, leading vs lagging indicators, guardrail metrics, and avoiding Goodhart's Law.
Diagnose a Metric Drop
Systematic approach to root-causing a DAU or revenue drop: external factors, internal changes, data pipeline issues, user segment breakdown.
North Star Metric Framework
How to identify a product's North Star Metric, the relationship to business outcomes, and common mistakes.
Design a Product for a New Market
Design a product for an underserved population (elderly, rural, emerging market). Tests user empathy, market sizing, and constraint-aware design.
Favorite Product & Why
Articulate what makes a product great using structure: user, problem, solution, why it works.
Improve a Product (Google Maps)
Classic "improve a product" question. Covers user segmentation, pain point identification, feature ideation, and prioritization.
Product Design Framework (CIRCLES)
Structured approach to product design questions: Comprehend situation, Identify customer, Report needs, Cut through prioritization, List solutions, Evaluate trade-offs, Summarize.
Market Entry Strategy
Framework for entering a new market: TAM/SAM/SOM, competitive landscape, positioning, entry barriers, and go-to-market.
Describe a Technical Decision You Made Under Pressure
STAR format: the constraint, your analysis, the decision, trade-offs, and outcome. Show sound engineering judgment.
Tell Me About a Time You Disagreed with Your Manager
Show you can advocate for your view with data, respect hierarchy, and execute even when overruled.
Arrays & Hashing Patterns
Two-sum, prefix sums, sliding window, frequency maps. Core patterns for O(n) array problems.
Binary Search & Variants
Classic binary search, rotated arrays, search in sorted matrix, bisect_left/right patterns. When to use and how to derive the invariant.
Dynamic Programming Patterns
Top-down vs bottom-up, memoization, 1D/2D DP, interval DP, knapsack pattern. Key: identify state + recurrence.
Graph Algorithms (BFS, DFS, Dijkstra)
Adjacency list representation, cycle detection, topological sort, shortest path (Dijkstra, Bellman-Ford), union-find.
Trees: DFS & BFS Traversals
Pre/in/post-order DFS, level-order BFS, path problems, LCA, diameter of binary tree.
Concurrency Primitives
Threads vs processes, mutex/semaphore, deadlock detection, race conditions, async/await vs threads.
Database Indexing & Query Optimization
B-tree vs hash indexes, composite indexes, query explain plans, N+1 problem, covering indexes.
Design a Distributed Cache
Cache eviction policies (LRU/LFU), consistent hashing, replication, cache invalidation strategies.
Design a News Feed (Twitter/LinkedIn)
Fan-out on write vs read, celebrity problem, ranking signals, pagination, real-time updates, storage at scale.
Design a Rate Limiter
Token bucket vs sliding window vs fixed window algorithms. Distributed rate limiting with Redis. Edge cases.
Design a URL Shortener
Classic system design: encoding strategy, DB schema, read-heavy scaling (caching + CDN), analytics, fault tolerance.
Escalation and Conflict Resolution
When and how to escalate: data-driven framing, options presented, decision-maker identification, preserving relationships.
Driving Alignment Across Teams
Techniques for aligning multiple teams without direct authority: shared charter, RACI, working agreements, escalation protocols.
Managing External Dependencies
How to handle dependencies on partner teams, third-party vendors, or other BUs: tracking, escalation, mitigation plans.
Critical Path Analysis
Identify critical path in a project, dependencies, slack time, and how to compress schedule or unblock blockers.
OKR Framework for Technical Programs
Structuring OKRs for engineering programs: outcome vs output, cascading OKRs, key result design, and common pitfalls.
Program Status Reporting
RAG (Red/Amber/Green) status, executive summaries, milestone tracking, and how to communicate bad news early.
Risk Management in Large Programs
Risk identification, likelihood × impact matrix, mitigation strategies, contingency planning, and RAID logs.
API Design & Versioning
REST vs GraphQL, API versioning strategies, backward compatibility, SLA considerations — useful for TPMs owning platform programs.