← Anthropic Interview Insights
This is the core question and it ate the whole session.
Start by clarifying requirements and scope with the interviewer, then propose a high-level architecture that separates concerns: frontend for authoring and visualization, backend for orchestration and persistence, and a scalable execution layer for LLM calls and batch evaluations. Dive into key components like versioning, model comparison, and batch processing, discussing trade-offs and potential bottlenecks.
Pro tip: Emphasize idempotency and caching for LLM calls to avoid redundant costs and ensure reproducibility, and discuss how you'd handle rate limits and failures gracefully with retries and fallbacks.
Ask questions to understand expected scale, supported models, dataset sizes, and user roles. Define MVP vs. future features to prioritize.
Outline the main components: web UI, API gateway, prompt service, execution engine, storage for prompts/versions/results, and a queue for batch jobs. Choose appropriate technologies (e.g., React, Node.js, PostgreSQL, Redis, S3).
Explain how to implement prompt authoring with variables, versioning (e.g., immutable versions with metadata), model comparison (parallel calls, diff view), and batch evaluation (job scheduling, result aggregation).
Discuss handling high concurrency, rate limiting, caching, retries, and cost management. Consider using serverless functions or containerized workers for LLM calls.
Summarize key trade-offs (e.g., consistency vs. availability, cost vs. latency) and suggest potential enhancements like collaboration, analytics, or integration with CI/CD.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with SSE over WebSocket because the data flow is one-directional and SSE is simpler to implement and proxy.
Start by clarifying the requirements: sub-second first-token latency, streaming protocol, and scale. Then walk through the end-to-end pipeline from client to model, highlighting where latency is introduced and how to minimize it at each stage. Emphasize trade-offs between latency, cost, and complexity, and propose a concrete architecture with specific technologies.
Pro tip: Mention that you would measure and monitor time-to-first-token (TTFT) separately from inter-token latency, and set up a feedback loop to continuously optimize. Also, note that the first token often depends on prompt processing time, so techniques like prompt caching and pre-warming can be game-changers.
Ask about expected scale, latency targets, model size, and client constraints (e.g., browser support, network conditions). Confirm that sub-second TTFT is the primary goal and understand the acceptable trade-offs.
Break down the flow: client request -> load balancer -> API gateway -> inference service -> model -> token streaming back. Identify potential bottlenecks at each hop, such as network latency, queuing, and model loading.
Propose specific optimizations: use HTTP/2 or WebSockets for streaming, deploy inference servers close to users (edge), use smaller/faster models or speculative decoding, and implement prompt caching to reduce prefill time.
Choose a streaming protocol (e.g., Server-Sent Events, WebSockets) and describe how the client will render tokens incrementally. Discuss backpressure, error handling, and reconnection strategies.
Discuss trade-offs between latency, cost, and accuracy (e.g., using a smaller model). Explain how you would monitor TTFT and inter-token latency, and iterate based on metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose a data model that separates prompt templates from versions, with named variables and team sharing. Discuss trade-offs in storage, versioning, and access control, and outline how you would handle updates and collaboration.
Pro tip: Emphasize immutability of versions and the use of content-addressable storage to ensure reproducibility and simplify sharing. Also, mention the importance of a migration strategy for template changes.
Ask questions to understand scale, expected features (e.g., variable types, versioning semantics), and team collaboration needs. This ensures the design meets actual use cases.
Propose a schema with separate entities for templates, versions, variables, and teams. Consider using a document store or relational DB with JSON columns for flexibility.
Decide on versioning approach: immutable versions with unique IDs, semantic versioning, or content hashing. Discuss how to handle updates, rollbacks, and diffs.
Outline permissions model (e.g., RBAC) and sharing mechanisms (e.g., team workspaces, public links). Address how to handle concurrent edits and notifications.
Discuss trade-offs between consistency and availability, storage costs, and query performance. Mention caching, indexing, and potential sharding for scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rushed this because we were running low on time.
Start by clarifying requirements and constraints, then outline a high-level architecture covering data ingestion, prompt execution, result storage, and output surfacing. Walk through each component, emphasizing scalability, reliability, and observability, and finish by discussing trade-offs and potential optimizations.
Pro tip: Proactively discuss how you would handle rate limits, retries, and cost controls when calling the LLM API, as these are critical for production batch pipelines at scale.
Ask about dataset size, expected throughput, latency requirements, budget, and how per-row outputs should be surfaced (e.g., UI, CSV, database).
Choose a format for the dataset (e.g., JSONL, CSV) and a storage solution (e.g., S3, database) that supports efficient batch reads and writes.
Build a worker pool that loads the saved prompt, applies it to each row, calls the LLM API with concurrency control, and handles retries and errors.
Persist results (e.g., in a database or file) with row identifiers, and provide an interface (e.g., API, dashboard) to view outputs per row.
Instrument the pipeline with metrics (success rate, latency, token usage) and logs, and implement cost tracking to ensure budget adherence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about token counting at the response level and writing usage records to a ledger table.
Start by clarifying requirements: what granularity of tracking (per request, per token, per API call), what time windows for rate limiting, and whether enforcement is hard or soft. Then propose a design that separates the data plane (fast, in-memory counters for rate limiting) from the control plane (durable, aggregated usage records for billing/analytics), and discuss trade-offs between accuracy, latency, and cost.
Pro tip: Emphasize idempotency and eventual consistency: use idempotent writes to avoid double-counting on retries, and accept slight over/under-counting in rate limiting for performance, but ensure billing data is reconciled asynchronously. This shows you understand real-world distributed systems constraints.
Ask about scale (requests per second, number of users/teams), required accuracy (exact vs approximate), latency sensitivity, and whether rate limiting should be global or per-region. Also clarify what metrics matter (API calls, tokens, compute time).
Propose a schema for usage events (user_id, team_id, timestamp, metric_type, quantity) and aggregated counters (per user/team, per time window). Choose storage: e.g., time-series DB or OLAP for analytics, Redis for real-time counters, and a relational DB for billing records.
Use a fast in-memory store (e.g., Redis) with atomic increments for per-user/team counters. For rate limiting, implement a sliding window or token bucket algorithm, and enforce limits at the API gateway or service mesh layer to minimize latency.
Asynchronously persist usage events to durable storage (e.g., Kafka + stream processing) for accurate billing. Implement idempotent writes and periodic reconciliation to correct any drift between real-time counters and durable records.
Address trade-offs: accuracy vs latency, cost of storage vs query performance, and complexity of distributed rate limiting. Cover failure modes: Redis outage, network partitions, and how to degrade gracefully (e.g., fall back to local counters with eventual consistency).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said to use a secrets manager like Vault or a cloud-native equivalent, never store raw API keys in the app database.
Start by clarifying the multi-tenant requirements and threat model, then propose a centralized secrets management system with per-tenant isolation and dynamic credential injection. Emphasize defense-in-depth: encryption at rest and in transit, least privilege, audit logging, and automated rotation. Finally, discuss trade-offs between security, latency, and operational complexity.
Pro tip: Mention that you would never store secrets in code or environment variables for production, and that you would use a dedicated secrets manager like HashiCorp Vault or AWS Secrets Manager with tenant-specific paths and IAM policies. Also highlight the importance of short-lived credentials and just-in-time access to minimize blast radius.
Ask about the number of tenants, compliance needs (e.g., SOC2, GDPR), and the sensitivity of LLM provider credentials. Identify potential attack vectors such as insider threats, compromised tenants, or leaked keys.
Propose a dedicated secrets manager (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) that supports encryption, access control, and audit logging. Ensure it integrates with your existing identity provider for authentication.
Store each tenant's credentials in separate paths or namespaces with strict IAM policies so tenants can only access their own secrets. Use role-based access control (RBAC) and attribute-based access control (ABAC) to enforce least privilege.
Inject secrets at runtime via sidecars, init containers, or SDKs that fetch secrets on demand, avoiding hardcoding. Automate rotation with short-lived tokens and just-in-time access to reduce exposure.
Discuss monitoring, alerting, and auditing for secret access. Balance security with latency and complexity, and consider caching strategies with TTLs to avoid performance bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cache key is a hash of the full prompt content plus all parameters.
Start by clarifying the requirements: what types of prompts and parameters, expected scale, latency and cost constraints, and tolerance for stale results. Then propose a caching layer that hashes the normalized prompt and parameters to create a cache key, and discuss storage options (in-memory, Redis, etc.) and eviction policies. Finally, address trade-offs such as cache invalidation, consistency, and privacy considerations.
Pro tip: Emphasize that caching LLM responses is not just about performance but also about cost savings and reducing carbon footprint, which aligns with Anthropic's responsible scaling principles. Also, mention that you would monitor cache hit rates and adjust TTLs based on usage patterns.
Ask about the expected volume of requests, diversity of prompts, latency requirements, and whether stale responses are acceptable. This ensures the caching strategy fits the use case.
Explain that you would normalize the prompt (e.g., trim whitespace, lowercase) and serialize parameters in a deterministic order, then hash the combination (e.g., SHA-256) to create a unique cache key.
Select a cache store (e.g., Redis, Memcached, or in-memory LRU) based on scale and latency. Define an eviction policy (e.g., LRU, TTL) to manage memory and staleness.
Discuss how to invalidate entries when prompts or parameters change, and consider versioning the cache key to avoid stale responses. Mention trade-offs between consistency and availability.
Propose tracking cache hit/miss rates, latency, and cost savings. Use this data to tune TTLs, eviction policies, and possibly implement tiered caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Circuit breaker pattern per provider, with a fallback flag on the request that lets the UI know a provider is degraded.
Start by clarifying the system architecture and failure modes, then walk through a layered degradation strategy that prioritizes user experience and safety. Emphasize trade-offs between fallback providers, caching, and feature reduction, and tie your answer to Anthropic's focus on reliability and responsible AI.
Pro tip: Frame graceful degradation as a product decision, not just an engineering one—discuss how you'd communicate reduced capability to users and set expectations to maintain trust.
Ask questions to understand the architecture: Is there a primary LLM provider with fallbacks? What's the criticality of the feature? Define what 'down' means (latency, errors, rate limits).
Outline a tiered response: from full functionality to reduced quality (e.g., smaller model, cached responses), to non-LLM fallbacks (rule-based), to graceful error messages.
Describe how to detect failures (health checks, circuit breakers) and route to alternative providers or models, considering cost, latency, and capability differences.
Explain how to manage in-flight requests, retries with idempotency, and communicate degradation to users (e.g., banners, adjusted expectations) without breaking trust.
Emphasize observability (metrics, logs) and chaos testing to validate degradation paths, and continuous improvement based on incidents.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.