This is one of those problems that sounds manageable until you realize the scope.
Start by clarifying requirements and constraints, then design the gateway as a stateless, horizontally scalable service with a unified API schema that abstracts provider-specific differences. Focus on core components like request routing, provider adapters, and observability, and discuss trade-offs around latency, cost, and reliability.
Pro tip: Emphasize idempotency and graceful degradation: ensure requests can be safely retried and that the gateway can fall back to alternative providers or cached responses during outages, which is critical for production reliability.
Ask about expected traffic volume, latency SLAs, supported providers, authentication needs, and whether features like streaming, caching, or rate limiting are required. This ensures the design meets actual business needs.
Design a single API schema (e.g., REST or gRPC) that normalizes inputs/outputs across providers, including common parameters like model, temperature, and max tokens. Use an adapter pattern to translate between the unified API and each provider's specific API.
Outline key components: API gateway (auth, rate limiting), request router (load balancing, failover), provider adapters, response cache, and observability (logging, metrics, tracing). Describe the request flow from client to provider and back.
Discuss horizontal scaling, circuit breakers, retries with exponential backoff, and idempotency keys. Cover security aspects like API key management, encryption in transit, and compliance with data privacy regulations.
Compare trade-offs: latency vs. cost (e.g., caching vs. real-time), consistency vs. availability, and vendor lock-in vs. flexibility. Mention optimizations like batching, streaming, and dynamic provider selection based on cost or performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through exponential backoff, circuit breaker states, and rerouting to a secondary provider.
Start by framing the problem as building a resilient LLM gateway that abstracts provider interactions, then walk through detection, mitigation, and recovery strategies. Emphasize trade-offs between latency, cost, and reliability, and tie your choices to Decagon's conversational AI use case where user experience is paramount.
Pro tip: Mention that retries should be idempotent and use exponential backoff with jitter, but for LLM calls, also consider token limits and cost implications—sometimes failing fast and falling back to a cheaper model is better than retrying an expensive one.
Explain how you monitor error rates, latency percentiles, and timeouts per provider. Use these signals to trigger circuit breakers and failover.
Describe using a circuit breaker pattern (e.g., with thresholds for error rate and timeout) to stop sending traffic to a failing provider. Fallback to a secondary provider or a degraded response.
Detail retry policies: exponential backoff with jitter, max retries, and idempotency keys. Avoid retrying on non-retryable errors (e.g., 4xx).
Discuss fallback strategies: cached responses, simpler models, or queuing requests. Communicate status to users if needed.
Emphasize observability, chaos testing, and tuning thresholds based on real traffic. Continuously improve based on post-mortems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Hedged requests were the interesting bit here.
Start by clarifying the latency budget and user experience goals, then structure your answer around a layered strategy: reduce work (caching, batching), overlap work (parallel calls, hedging), and optimize the critical path (connection reuse, protocol choices). Emphasize trade-offs like cost, complexity, and consistency, and tie your choices to measurable SLOs.
Pro tip: Quantify the impact of each technique with rough numbers (e.g., 'hedging can cut p99 by 30-50% at the cost of 10-20% more requests') and mention that you'd validate with load tests and real user monitoring before rolling out broadly.
Establish the target latency (e.g., p95 < 200ms) and understand the system's dependencies, failure modes, and cost constraints. This frames which strategies are worth the complexity.
Apply caching at multiple layers (client, CDN, application, database) and use techniques like request coalescing, batching, and precomputation to avoid redundant work.
Make independent calls concurrently, use request hedging (send duplicate requests to multiple replicas/providers and take the first response), and consider speculative execution for predictable follow-up calls.
Reduce network overhead with connection pooling, keep-alive, HTTP/2 or gRPC, and edge deployment. Tune timeouts and retries to avoid cascading delays.
Instrument end-to-end latency, run A/B tests or canary releases, and balance latency gains against increased cost, complexity, and potential consistency issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a usage ledger approach, writing token counts per request tagged to a tenant ID and model.
Start by clarifying requirements: what granularity of accounting (per request, per token type), which providers, and what budget enforcement actions (hard stop, alert, throttle). Then propose a unified data model that normalizes token usage and cost across providers, and describe the pipeline from request interception to aggregation and enforcement.
Pro tip: Mention that you'd store raw token counts and apply cost multipliers at query time, not at ingestion, so pricing changes don't require backfilling historical data. Also, use idempotency keys to avoid double-counting on retries.
Ask about required granularity (per request, per token type), latency tolerance for enforcement, and whether budgets are hard or soft. Identify all LLM providers and their pricing models (per token, per character, etc.).
Define a normalized schema for usage events: tenant_id, api_key_id, provider, model, input_tokens, output_tokens, timestamp, request_id, and metadata. Store raw counts and compute cost via a separate pricing table to allow updates without backfilling.
Intercept requests at a gateway or middleware layer to capture usage from provider responses. Use idempotency keys and async logging to avoid impacting latency. For streaming responses, accumulate tokens as they arrive.
Stream usage events to a fast aggregation store (e.g., Redis) for real-time budget checks, and to a data warehouse for analytics. Enforce budgets by checking current spend against limits before or during requests, with configurable actions (block, throttle, alert).
Address retries, failures, and provider discrepancies (e.g., token counting differences). Ensure the system is scalable, fault-tolerant, and provides audit trails. Consider eventual consistency for analytics vs. strong consistency for enforcement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Schema enforcement was easy to talk about.
Structure your answer around a layered validation pipeline: first, enforce structured output with schema validation and retries; second, assess semantic accuracy using automated checks and human-in-the-loop; third, continuously monitor provider quality with canary tests and statistical drift detection. Emphasize trade-offs between latency, cost, and reliability, and how you'd design experiments to catch degradation early.
Pro tip: Treat LLM providers as unreliable dependencies: implement circuit breakers and fallback models, and always log raw responses with request IDs for debugging and provider accountability. This shows you think about production resilience, not just correctness.
Use JSON schema validation, Pydantic models, or function calling to constrain responses. On validation failure, retry with a repair prompt or fallback to a stricter model.
Combine automated checks (e.g., unit tests, fact verification against knowledge bases, consistency checks) with human review for high-stakes outputs. Use LLM-as-a-judge for scalable scoring.
Run canary tests with golden datasets, track metrics like schema adherence, latency, and semantic scores over time. Set up alerts for statistically significant drops.
A/B test providers or model versions, use shadow deployments to compare outputs, and implement automatic failover to backup providers when quality degrades.
Feed validation failures back into prompt engineering, fine-tuning, or provider selection. Continuously refine thresholds and test suites based on production data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the gateway's role and requirements, then structure your answer around rate limiting, quota management, observability, and audit logging. For each component, explain the design choices, trade-offs, and how they integrate to provide a robust and scalable solution.
Pro tip: Emphasize the importance of distributed rate limiting and how you'd handle synchronization across multiple gateway instances, as this is a common pitfall. Also, highlight the need for audit logs to be immutable and tamper-evident for compliance.
Ask about expected traffic volume, latency requirements, multi-tenancy, and compliance needs. This ensures your design is tailored to the specific context.
Choose an algorithm (e.g., token bucket, sliding window) and decide on a distributed store (e.g., Redis) for shared state. Discuss how to handle bursts and fairness.
Define quotas per user, API key, or tenant, and enforce them over longer periods (e.g., daily/monthly). Explain how quotas are tracked, reset, and how to handle overages.
Instrument metrics (e.g., request rates, error rates, latency), logging, and tracing. Use tools like Prometheus, Grafana, and Jaeger to monitor and alert.
Log all access and configuration changes with sufficient detail (who, what, when, where). Store logs securely and immutably for compliance and forensics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.